Potentials of ecosystem service accounting at
multiple scales
Zurlini G.*, Jones K.B.^, Li B.-L.**, Petrosillo I*
*Landscape Ecology Laboratory, Dept. of Biological and Environmental Sciences and Technologies, University of
Salento, Lecce, Italy.
^US Geological Survey, Reston, Virginia, USA.
**Ecological Complexity and Modeling Laboratory, Dept. of Botany and Plant Sciences, University of California at
Riverside, Riverside, CA, USA
Why valuating ecosystem services at multiple scales?
To acknowledge the value of natural capital, one of society’s important assets, a better
understanding of the patterns and spatial scales at which ecosystem services operate is
essential to developing ecosystem services valuation (ESV) to support landscape-level
conservation and land management plans.
Ecosystem services (ESs) are benefits that can be provided by human-modified and
natural systems (Costanza et al. 1997; MEA 2003). The structure and function of such
systems represent a stock of natural capital providing flows of goods (such as food, timber,
forage, and non-traditional forest products) and services (such as soil formation and
nutrient/carbon storage, erosion control, water filtration, pollution assimilation, and
recreation) that are valued by society. These goods and services constitute a large share
of our social and economic welfare, and are vital to the Earth’s life-support system
(Costanza et al. 1997) (Table 1). Biodiversity is a critical part in providing ecosystem
services in that most are due to the component populations, species, functional groups
(guilds), food webs, habitat types or mosaics of habitats and land uses that collectively
produce them, i.e., the ecosystem service providers (the ESPs) (Kremen 2005, Feld et al.
2009).
One of the greatest challenges facing humanity involves the understanding of distinct
scales of environmental change and human response (Folke et al. 2005), and how the
interplay of those scales has evolved historically, which is of paramount importance to get
a better insight into how specific geographic areas are interrelated (Costanza et al. 2007).
A clear understanding of scale can lead to development of more effective environmental
policies and management. It can tell the local land owners and jurisdictions whether or not
they can protect and enhance ESs locally (through their actions alone) or whether they
must rely on regional, national, or global environmental policies to achieve and sustain
certain levels of ESs. For example, reduction of hypoxic or anoxic conditions in estuaries
and near-shore marine environments requires reduction of nutrient inputs coming off of
entire river basins. As such, a comprehensive understanding of scale relations across
entire river basins is needed to improve and maintain estuarine and near-shore habitats.
Human activities have become so extensive that they have increased the interconnectivity
of our global social-ecological system. This increased interconnectivity has resulted in a
global scale alteration of some of our most important ESs (Cronin 1996; Vitousek et al.
1997). Resulting changes in ESs are caused by multiple interacting direct drivers (MEA
2005) such as land cover change, global changes in atmosphere and climate from
industrial emissions, water use and availability due to irrigation, alien invasive species
expansion, and local changes in species populations due to harvesting, habitat destruction
and landscape degradation. Direct drivers operate at multiple scales and, in turn are
controlled by indirect drivers (e.g., economic, demographic, or cultural changes) (MEA
2003) operating more diffusely, by altering one or more direct drivers. An example of how
climate variabilty/change is magnified by impervious surface created through local or
county level land use change (e.g., transition to urban and developed from agricultural
lands) is given by Jennings and Jarnagin (2002). Land-cover change has been identified
as one of the most important drivers of change in ecosystems and their associated
services, representing a primary human effect on natural systems and underlying
fragmentation and habitat loss, which are the greatest threats to biodiversity (Vitousek et
al. 1997; MEA 2005).
At the landscape scale, the dynamic spatial configuration resulting from human
appropriation and management of regional landscapes can have a variety of ecological
effects over a wide range of spatial scales. A direct effect is the alteration of ecological
processes at local scales through the modification of land cover. For example, converting
forest to agriculture land cover alters soil biophysical and chemical properties and
associated animal and microbial communities, and agricultural practices such as crop
rotation alter the frequency of these disturbances. The spatial configurations of land cover
in a region also affect ecological patterns and processes. This is one of the fundamental
underlying concepts coming out of the field of landscape ecology that is spatial distribution
and pattern affect important ecological flows (e.g., nutrients/materials, biota, energy,
water) that sustain certain levels of ESs and their associated benefits. New land cover
types can be juxtaposed and shifted within increasingly fragmented remnant native land
cover types, and changes in the structure of the landscape can alter (in a negative way)
nutrient transport and transformation (e.g. Peterjohn and Correll, 1984), species
persistence and biodiversity (e.g. Tilman et al., 2002; Benton et al., 2003), and nurture
invasive species (e.g. Fox and Fox, 1986; With, 2004).
Based on the last decade of research, we are beginning to understand the complex ways
in which humans have affected and have been affected by natural systems of the Earth
(Steffen et al. 2004, Diamond 2005, MEA 2005). Much work has been oriented to describe
and categorize ESs, identify methods for economic valuation, mapping the supply and
demand for services, assessing threats, and estimating economic values (Costanza et al.
1997; Daily 1997; MEA 2003; MEA 2005).
There is a growing demand for answers to questions like: How much of a watershed’s
(catchment) area must be forest and where are the most important areas to maintain or
restore in order to provide clean water for downstream communities? How should patches
of different land uses/land covers be distributed within a rural landscape to provide flood
regulation or pollination and pest control services for crops? Up to what distances might
adjacent land uses affect the capacity of natural habitat to provide pest control and
pollination services? Answers to these questions will help determine how different land
uses and set-asides (protected designations) should be spatially distributed in the
landscape in order to protect and manage ESs.
Ecosystem services valuation (ESV) is the process of assessing the contributions of ESs
to certain sustainable scales, fair distribution, and efficient allocation of space and
resources (Liu et al. 2010), like having an appropriate mix of land-use types across scales
such that ESs can be maintained. There is also the need to move from general estimates
provided by Costanza et al. (1997) to more specific statements at regional and local
scales. To effectively manage changes in the flow of goods and service we need to
incorporate ESV into resource management decisions. However, moving from general
statements about the tremendous benefits nature provides to people to credible,
quantitative estimates of ESs has proven difficult (MEA 2005).
Among the crucial issues for obtaining reliable, quantitative estimates of ESs are those
related to scales and interaction between services. Goods and services that humans
obtain from ecosystems may span different scales in space and time, and can interact with
one another in complex, often unpredictable ways. Improvements in the understanding of
the patterns at multiple spatial and temporal scales at which ecosystem services operate
are likely to provide more realistic ES values and also to improve ecosystem-based
management practices.
Scales of operation of the services
Most ESs are broadly classified as operating on local, regional, global or multiple scales,
and different providers of the same ES may operate across a range of spatial and
temporal scales (Costanza, 2008; Ricketts et al., 2008). Services can be localized, e.g.,
fruit from a single tree, or derived from a relatively large area, e.g., flood control by
wetlands or climate regulation through carbon sequestration (Costanza et al. 1997; Daily
1997; Costanza 2008). ES providers are landscape- or habitat-wide or ecological
community attributes, and can often be characterized by the component populations,
species, functional groups (guilds), food webs or habitat types that collectively produce the
services (Kremen 2005) (Table 2). They can operate at different scales. So, for example,
pollinators or native predators providing pest control on crops generally operate at a local
scale, whereas forests contribute to climate regulation at local (shading), regional (rainfall
patterns and albedo) and global (carbon sequestration) scales.
Costanza (2008) has recently classified the spatial characteristics of 17 ESs listed in
Costanza et al. (1997) based not only on the scale of operation of the service, but also on
the spatial proximity of service delivery to human beneficiaries (Table 3). Services like
carbon sequestration, which is an intermediate input to climate regulation, is classified as
global non-proximal. This is because the atmosphere is generally well-mixed and removing
carbon dioxide (or other greenhouse gases) at any location is equivalent to removing it
anywhere else. However, at local or even regional scale, in certain areas and during
certain periods of the year, conditions of stratified thermal inversion may occur in the lower
atmospheric layers resulting in respiratory distress and infections in people.
Local proximal services, on the other hand, are dependent on the spatial proximity of the
ecosystem to the human beneficiaries. Use values will be determined by local patterns of
land use, human population settlements and proximity to beneficiaries. The recreational
function of a landscape or ecosystem, for example, is not only defined by the land cover of
a specific location (e.g. natural area) but depends also on accessibility properties (e.g.
distance to roads) and the characteristics of the surrounding landscape. Also pollination
requires that the ecosystem with pollinators be proximal to the land being pollinated.
Directional flow-related services are dependent on the flow from upstream to down-stream
as is the case for water supply and water regulation. This kind of service is the basis for
ecosystem service payments established in 1996 in Costa Rica to induce landowners to
provide ecosystem services. In this case, downstream users pay upstream landowners to
maintain forest cover on their land to benefit from protection of upstream ecosystem
services (MEA 2005). Other examples of this kind of service can be found where, like in
Italy, the economic value of non-marketed services provided by forests for watershed
protection and slope stability against landslides, can be very high (MEA 2005).
The effects of land-use intensity on local biodiversity and ecological functioning depend on
spatial scales much larger than a single field or land use/land cover. This demands a
landscape perspective, which takes into account the spatial arrangement of surrounding
land-use types at multiple scales. To be characterized adequately, pattern–process
relationships must be assessed at the multiple scales (Turner et al. 2001, Levin, 1992)
relevant to the inherent structure (or rate) of the system (or process) being studied (Holling
1992), or the scale of perception of the organism(s) (Wiens 1989; Wiens and Milne 1989)
providing the service (ESPs). In this respect, spatially explicit estimates of ES efficiency
across landscapes at multiple scales that might inform land-use and management
decisions are still lacking (Balmford et al. 2002; MEA 2005) (Table 4).
Non-linearity
The valuation process of ecosystem services (ESV) to more accurately represent their
value needs to incorporate the non-linear properties of these services over space and time
(Kock et al. 2009). In many field settings, like rural landscapes, there is a nested set of
cycles, each occurring over its own range of scales (Holling et al. 1996). Some cycles
occur annually, some take around a decade, and still others may take a century.
Photosynthetic activity, for example, is a fundamental process necessary for the
production of other ecosystem services like carbon fixation, oxygen and primary
production. Photosynthetic activity can be highly variable over time and space, but it is
often assumed to change linearly, i.e. at a steady, unvarying rate for each habitat
considered. When it is measured by green index (NDVI) from satellite data it shows rather
distinct and characteristic seasonal inter-annual periodicities for different broad landuse/land-cover categories corroborating well-known vegetation changes given by seasonal
variations in climate and water regime as well agricultural practices. For example, in Apulia
(south Italy) mean NDVI paths of land-use/land-cover show the greatest separation during
the hot and dry summers owing to drought of semi-arid grasslands, and water regulation of
forests (Zaccarelli et al. 2008).
Another example is coastal protection provided by wave attenuation by some sea grasses
that may be at its maximum during summer, when plants are reproducing, at medium
levels in spring and fall, and non-existent during winter, when density and biomass are low
(Kock et al 2009). Yet, in the Mediterranean, sandy beach erosion can be still mitigated
during winter by the stranding of seagrass leaves (Müller et al. 2008).
Non-linearity may occur also because of the interaction between services.
Synergism and trade-off among services
ESs may interact with one another in complex and unpredictable ways, and knowledge of
the interactions among ESs is necessary for making sound decisions about how society
manages the services. As to directional flow-related services, impounding streams for
hydroelectric power, for example, may have negative consequences on downstream food
provisioning by fisheries. In other words, a synergism occurs when ESs interact with one
another in a multiplicative or exponential way. Synergistic interactions can have positive
and negative effects, and pose a major challenge to the management of ESs because the
strength and direction of such interactions remains virtually unknown (Sala et al. 2005).
Trade-offs, in contrast, occur when the provision of one ES is reduced as a consequence
of increased use of another ES. Agricultural production shows an inverse relationship with
water quality and quantity, as we increase agricultural production, the quality of water and
the quantity available tend to decrease (MEA 2005). Thus, use of nutrients and pesticides
to increase agricultural production can lead to critical declines in water quality that can
often propagate down-stream. Trade-offs seem inevitable in many circumstances and will
be critical for determining the outcome of environmental decisions. In some cases, a tradeoff may be the consequence of an explicit choice; but in others, trade-offs arise without
premeditation or awareness that they are taking place. Managers must clearly identify
temporal trade-offs to allow policy-makers to understand the long-term effects of preferring
one ecosystem service over another. This is crucial for any planning activity. Decisions are
made to maintain provisioning services in the present, often at the expense of provisioning
services in the future, as many managers are rewarded for short-term success (MEA
2005). This is one of the most fundamental temporal cross-scale challenge we are facing.
Ecosystem services in social-ecological systems
Humans may interact with ecosystems at various scales as individuals or as
representatives of organizations responding to environmental changes (e.g., climate,
drought, desertification) through multiple pathways. Their responses may in turn alter
feedbacks between climate, ecological and social systems, producing a complex web of
multidirectional connections in time and space (Costanza et al. 2007). Highly interlinked
and connected systems transfer shocks through the system faster and more completely
than systems that are more modular and disconnected. The most important consequence
for our globally interconnected social-ecological system is that the dynamics of our
economic system(s) are no longer determined by economics alone, but by the dynamics of
the total environmental (economic and ecological) system (Limburg et al. 2002).
Anthropogenic disturbances such as changes in land use are determined by the social
component of social-ecological landscapes. Such component is made by groups of
people, organized in a hierarchy at different levels (e.g., household, village, county,
province, region, and nation), that result in a distinct set of spatial patterns and scales
influencing fundamental ecological processes (e.g., flows of materials/nutrients, water,
biota, and energy). These patterns and resulting processes formed by humans in turn
affect the quality and amount of ESs that a particular socio-ecological landscape can
provide. Any given land use system in the hierarchy is likely to overlap multiple ownership
and jurisdictional boundaries. Within this “panarchy” (Gunderson and Holling, 2002), the
participants can have differing views as to which set of ESs is desirable at each level of
the panarchy. For example, scale determines different values of wetland services in the
Netherlands for stakeholders at different jurisdictional levels (Hein et al. 2006). In this
example, at the municipal scale, interests refer to recreation, reed cutting and fisheries,
whereas at the provincial scale, main concerns are recreation, but also nature
conservation. At the national level, nature conservation is by far the most important
service.
Because of historical land-use legacies, decision hierarchies of social systems can often
be intertwined with the natural hierarchies, scales and frequencies that may emerge at the
ecosystem or landscape level (Gunderson and Holling 2002). As humans act as a
keystone species (sensu O’Neill and Kahn, 2000), the characteristic scales of particular
phenomena like anthropogenic changes are deemed to entrain and constrain ecological
processes that produce services, and to be related to the scales of human interactions
with the biophysical environment (Holling, 1992). If the patterns or scales of human land
use change, then the structure and dynamics of the system as a whole can change
accordingly, leading to transitions between alternative phases, when the integral structure
of the systems is changed (Kay, 2000; Li, 2000; Li 2002). In other words, people can
structure landscapes leading to a set of different ecological flows that result in different
levels (and types) of ESs. Alternatively, natural biophysical conditions can structure what
humans do on the landscape, for example, in mountainous regions where agriculture
occurs in the river bottoms or valleys but not on the mountains.
The emergence of new scales in the panarchy also adds to the complexity. The formation
and expansion of the European Union and the World Trade Organization globally, or
catchment management authorities and land-care groups locally can be examples of
relatively new institutions. All of these institutions create new scales of management and
interaction among services that influence and are influenced by the scales above and
below them, ultimately influencing how ecosystem services are managed on the ground.
Moreover, the degree to which this can be done depends also on whether the biophysical
setting constrains how humans develop and use the landscape. In Italy, for example, there
are not too many natural constraints but rather constrains of social and economic nature.
But in other places like Switzerland, there are significant biophysical constraints on what
gets developed due to mountainous landscapes.
Cross scale effects
Local processes are often spread and become important only when they merge at regional
(for example, large agricultural land aggregations) or global scales (for example, carbon
emissions that change the global atmosphere), but ecosystem services at more
aggregated scales are seldom simple summations of the services at finer scales
(Carpenter et al. 2006). Conversely, most services are delivered at the local scale, but
their supply is influenced by regional or global-scale processes. Often local communities
obtain some ecosystem services from other geographies. This is the origin of cross scale
effects.
The increasingly connected and dynamic nature of social-ecological systems means that
cross scale interactions are becoming more common. As these connections increase and
strengthen, cross scale effects penetrate further across the scale hierarchy. Gunderson
and Holling (2002) describe the way in which these linked scales influence each other,
drawing on numerous examples from ecological and social systems to articulate the
influence of cross scale effects. But it is in linked social–ecological systems where cross
scale effects become so critical that spatial mismatches are expected when the spatial
scales of management and the spatial scales of ecosystem processes do not align
properly leading to disruptions of social-ecological systems, inefficiencies, and/or loss of
important ecological components (Cumming et al. 2006). Habitats, for instance, are often
managed at a local level and hence are disassociated with other efforts in a region. Yet, to
maintain biological diversity, especially for animals like migratory birds, one needs to have
a management system that sets regional priorities. These regional priorities must then
constrain what is done at the local scale. Mismatches often occur as this almost never
happens.
Although there are many case studies, our capability of predicting emergence of crossscale effects and their impacts on ESs is limited (Carpenter et al. 2006). Currently, the
fundamental cross-scale challenge is the mismatch between the dynamics of natural
systems and the dynamics of human management systems (Levin 2000). Social and
ecological scales might be, but are not always, aligned. This can lead to failures in
feedback, when, for instance, benefits occur at one scale, but costs are carried at another.
A better understanding of the historical evolution of the web of connections and how to
adapt to future surprises will lead to the most appropriate future responses and feedbacks
within social-ecological systems (Costanza et al. 2007). To develop that understanding, we
need robust, manageable frameworks for analyzing ecosystem services at multiple time
and space scales. Multi-scale assessment at any scale will be improved by information
and perspectives from other scales (MEA 2005).
Limits of reductionist approaches
Theories and approaches to ecosystems and landscapes have to a large extent focused
on single issues or resources and been based on single scale or a few different distinct
scales with a steady-state view, interpreting change as gradual and incremental, in most
cases disregarding patterns across a continuum of scales and interactions across scales
with social-economic components.
Also addressing the social and economical dimensions of landscapes without an
understanding of resource and ecosystem dynamics will not be sufficient to lead society to
sustainable outcomes. In interlinked social-ecological systems (Berkes and Folke, 1998) it
may be easiest to analyze social and ecological attributes and functions separately.
However, such an approach may be at the expense of changes in the capacity of
ecosystems to sustain the adaptation and it could affect the quality of ecosystem goods
and services. This is because it could degrade natural renewable and non-renewable
resources and generate traps and breakpoints in the whole system eventually leads to a
transition to a new phase that results in degraded levels of ESs (Walker and Meyers,
2004).
Such partial approaches are less likely to be useful under global environmental change
wherein the capacity of many ecosystems to generate ESs for human welfare has become
vulnerable and no longer can be taken for granted (Folke et al. 2005).
Current approaches to ecosystem service valuation
There are two main approaches for the generation of ES valuation (ESV) that aim to
influence policy decisions (Nelson et al. 2009).
According to the first approach, researchers use broad-scale assessments of multiple
services to extrapolate a few estimates of values, usually derived from the coefficients
based on habitat types, such as those presented by Costanza et al. (1997), to entire
regions or the entire planet (e.g. Troy and Wilson 2006; Turner et al. 2007). Although
straightforward, this approach assumes that every hectare of a given habitat type is of
equal value – regardless of its quality, rarity, spatial configuration, size, proximity to
beneficiaries, or the prevailing social practices and values. This approach assumes that
ESs are changing linearly, i.e. at an unvarying rate for each habitat considered, and does
not allow for analyses of service provision and changes in value under new conditions. For
example, if a forest is converted to agricultural land, how will this affect the provision of
clean drinking water, downstream flooding and fish diversity, and soil fertility? Without
information on the consequences of land-use management practices on ES production, it
is hard to design policies or payment programs to foster the desired ESs.
In contrast, according to the second approach for generating policy-relevant ES
assessments, researchers carefully model the production of a single service in a small
area through “ecological production function” to relate the provision of that service to local
ecological variables (e.g. Ricketts et al. 2004). However, studies of biodiversity–function
often examine communities whose structures differ markedly from those providing services
in real landscapes (Diaz et al. 2003; Symstad et al. 2003), and have been restricted to a
small set of ecosystem processes (Schwartz et al. 2000).
While each of those approaches have provided many valuable insights, a bridge is needed
between these two approaches, i.e., one that will provide fundamental, ecological
understanding of ecosystem services to assist in realizing the best management and
policy tools for their conservation and sustainable use. What is needed are new
approaches joining the rigor of the small-scale studies with the extent of broad-scale
assessments (Boody et al. 2005; Nelson et al 2009) adopting a landscape perspective.
Perspectives in ecosystem service valuation
Can we manage our social–ecological systems at the local, regional, national or even the
global scale to be able to cope with shocks or influences that may come from any one of
the multitude of scales that we are now inextricably linked through our increasingly
interconnected society? In this direction, here are few points to address:
Relationships among multiple ecosystems services are better identified and assessed by
integrated social-ecological approaches than with either social or ecological data alone
(e.g. drivers of change). In this respect, multiple classifications of ESs are welcome
(Costanza, 2008). Yet, we have to improve the understanding of the ecology behind the
provision of multiple ecosystem services and their interactions.
The fundamental cross-scale challenge is the mismatch between the dynamics of natural
systems and the dynamics of human management (Folke et al. 2005). Social and
ecological scales might be, but not always, aligned. This can lead to failures in feedback,
when, e.g., benefits ensue at one scale, but costs are carried at another, like maintaining
provisioning services in the present often at the expense of provisioning services in the
future.
A better understanding of landscape legacies is needed, and of the historical evolution of
this web of connections (e.g. Costanza et al. 2007) to ensure appropriate future responses
and feedbacks within the human-environment system and how to adapt to future surprises.
The identification of common sets of correlated ESs and the situations (landscapes and
management regimes) in which they typically occur, and if services respond to the same
driver or they interact, can help reducing tradeoffs and creating synergies (Bennett et al.
2009).
To maintain multiple services, the maintenance of the component habitat types or
mosaics of habitats and land uses that collectively produce them rather than management
for individual ES provider species may be a more effective way of using limited resources
to benefit the greatest number of ES providers (coarse filter approach, Noss 1987).
We need integrated modeling tools that can allow predictions of the effects on biodiversity
on ESs from changes associated with land uses at multiple scales and organizational
levels, correlating changes in human well-being with past, present, and future changes in
climate and land-use.
Critical to progress in this area will be the development of spatially explicit landscape
models that consider horizontal as well as vertical flows in fundamental ecological
processes related to energy, materials/nutrients, water, and biotic fluxes and flows, and
how the spatial pattern and intensity of land use affect these flows.
We need rendering tool that displays ES vulnerability across multiple scales of key sectors
like agriculture, forestry, carbon storage and energy, water and biodiversity in order to
inform public policy at a variety of organizational levels
Stakeholder input can help quantify local and regional adaptive capacity, while climate and
landscape models can be used to estimate potential impacts. Adaptive capacity and
potential impacts together can help define the overall vulnerability of individual or common
sets of correlated ESs.
To effectively manage changes in the flow of goods and services, we must identify policy
levers that affect human behavior (such as zoning regulations, taxes, incentives, services,
and other infrastructure support) so that decision-makers can understand their effects on
the provisioning of ecosystem goods and services.
References
Balmford, A., Bruner, A., Cooper, P., Costanza, R., Farber, S., Green, R.E., Jenkins, M.,
Jefferiss, P., Jessamy, V., Madden, J., Munro, K., Myers, N., Naeem, S., Paavola, J.,
Rayment, M., Rosendo, S., Roughgarden, J., Trumper, K., Turner, R.K., 2002. Economic
reasons for conserving wild nature. Science 297: 950–53.
Balser, T.C., Kinzig, A., Firestone, M.K., 2001. Linking soil microbial communities and
ecosystem functioning. In: The Functional Consequences of Biodiversity: Empirical
Progress and Theoretical Extensions (eds Kinzig, A., Pacala, S.W. & Tilman, D.).
Princeton University Press, Princeton, pp. 265–293.
Balvanera, P., Kremen, C., Martinez, M., 2005. Applying community structure analysis to
ecosystem function: examples from pollination and carbon storage. Ecol. Appl.15: 360–
375.
Benton, T.G., Vickery, J.A., Wilson, J.D., 2003. Farmland biodiversity: is habitat
heterogeneity the key? Trends Ecol. Evolut. 18: 182–188.
Berkes, F., Folke C. (eds), 1998. Linking Social and Ecological Systems: Management
Practices and Social Mechanisms for Building Resilience. Cambridge, UK, Cambridge
Univ. Press.
Boody GB, Vondracek D, Andow M, et al. 2005. Multifunctional agriculture in the United
States. BioScience 55: 27–38.
Carpenter, S.R., DeFries, R., Dietz, T., Mooney, H.A., Polasky, S., Reid W.V., Scholes, R.
J., 2006. Millennium Ecosystem Assessment: Research Needs. Science 314: 257-258.
Costanza, R., d’Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Naeem, S.,
Limburg, K., Paruelo, J., O’Neill, R.V., Raskin, R., Sutton, P., van den Belt, M., 1997. The
value of the world’s ecosystem services and natural capital. Nature 387: 253–260.
Costanza, R., , Graumlich, L., Steffen, W., Crumley, C., Dearing, J., Hibbard, K., Leemans,
R., Redman, C., Schimel, D., 2007. Sustainability or Collapse: What Can We Learn from
Integrating the History of Humans and the Rest of Nature? Ambio 36 (7): 522-527.
Costanza, R., 2008. Ecosystem services: Multiple classification systems are needed. Biol.
Cons. 141: 350-352.
Cronin, W. (Ed.), 1996. Uncommon Ground; Rethinking the Human Place in Nature,
Norton, New York.
Cumming, G.S., Cumming, D.H.M. and Redman, C.L., 2006. Scale mismatches in socialecological systems: causes, consequences, and solutions. Ecology and Society 11(1), 14.
[online] URL: http://www.ecologyandsociety.org/vol11/iss1/art14/
Daily, G.C. (Ed). 1997. Nature’s services: societal dependence on natural ecosystems.
Washington, DC: Island Press.
Diamond, J. 2005. Collapse: How Societies Choose to Fail or Succeed. Viking, New York,
592 pp.
Diaz, S., Symstad, A.J., Chapin, F.S., Wardle, D.A. and Huenneke, L.F. (2003). Functional
diversity revealed by removal experiments. Trends Ecol. Evol., 18: 140–146.
Feld, C.K., da Silva P.M., Sousa, J.P., de Bello, F., Bugter, R., Grandin, U., Hering, D.,
Lavorel, S., Mountford, O., Pardo, I., Pärtel, M., Römbke, J., Sandin, L., Jones, K.B.,
Harrison, P., 2009. Indicators of biodiversity and ecosystem services: a synthesis across
ecosystems and spatial scales. Oikos 118: 1862-1871.
Folke, C., T. Hahn, P. Olsson, and J. Norberg, 2005. Adaptive governance of socialecological systems. Annual Review of Environment and Resources 30:441-473.
Fox, M.D., Fox, B.D., 1986. The susceptibility of communities to invasion. In: Groves, R.H.,
Burdon, J.J. (Eds.), Ecology of Biological Invasions: An Australian Perspective. Australian
Academy of Science, Canberra, Australia, pp. 97–105.
Gunderson, L.H. and Holling, C.S. (Eds), 2002. Panarchy: understanding transformations
in human and natural systems. Island Press, Washington, DC, USA.
Guo, Z.W., Xiao, X.M., Li, D.M., 2000. An assessment of ecosystem services: water flow
regulation and hydroelectric power production. Ecol. Appl., 10: 925–936.
Hein, L., van Koppen, K., de Groot, R.S., van Ierland, E.C. 2006. Spatial scales,
stakeholders and the valuation of ecosystem services. Ecological Economics 57: 209–
228.
Holling, C.S., 1992. Cross-scale morphology, geometry, and dynamics of ecosystems,
Ecological Monographs 62: 447-502.
Holling, C.S., Peterson, G., Marples, P., Sendzimir, J., Redford, K., Gunderson, L.,
Lambert, D., 1996. Self-organization in ecosystems: lumpy geometries, periodicities, and
morphologies. In: Walker BH, Steffen WL (eds), Global change and terrestrial ecosystems,
Cambridge University Press, Cambridge, UK, pp 346–384.
Jennings, D.B., Jarnagin, S.T., 2002. Changes in anthropogenic impervious surfaces,
precipitation and daily streamflow discharge: a historical perspective in a mid-atlantic
subwatershed. Landscape Ecol. 17(5): 471-489.
Jonsson, M., Dangles, O., Malmqvist, B., Guerold, F., 2002. Simulating species loss
following perturbation: assessing the effects on process rates. Proc. R. Soc. Lond. Ser. B,
269, 1047–1052.
Kay, J.J., 2000. Ecosystems as self-organizing holarchic open systems: narratives and the
second law of thermodynamics. In: S.E. Jørgensen and F. Müller (Editors), Handbook of
ecosystem theories and management, CRC Press – Lewis Publishers, pp. 135-160.
Koch, E.W., Barbier, E.B., Silliman, B.R., Reed, D.J., Perillo, G.M.E., Hacker, S.D.,
Granek, E.F., Primavera, J.H., Muthiga, N., Polasky, S., Halpern, B.S., Kennedy, C. J.,
Kappel, C.V., Wolanski, E., 2009. Non-linearity in ecosystem services: temporal and
spatial variability in coastal protection. Front Ecol Environ; 7(1): 29–37.
Kremen, C., Williams, N.M., Thorp, R.W., 2002. Crop pollination from native bees at risk
from agricultural intensification. Proc. Nat. Acad. Sci. USA, 99: 16812–16816.
Kremen, C., 2005. Managing ecosystem services: what do we need to know about their
ecology. Ecology Letters, 8: 468–479.
Kruess, A., Tscharntke, T., 1994. Habitat fragmentation, species loss, and biologicalcontrol. Science, 264: 1581–1584.
Larsen, T.H., Kremen, C., Williams, N., 2005. Extinction order and altered community
structure rapidly disrupt ecosystem functioning. Ecology Letters, 8: 538–547.
Levin, S.A., 1992. The problem of pattern and scale in ecology. Ecology 73: 1943-1967.
Levin, S.A., 1998. Ecosystems and the biosphere as complex adaptive systems.
Ecosystems 1: 431–436.
Levin, S.A., 2000. Multiple scales and the maintenance of biodiversity. Ecosystems 3:
498–506.
Li, B.L., 2000. Why is the holistic approach becoming so important in landscape ecology?
Landscape Urban Plan. 50 (1–3): 27–41.
Li, B.L., 2002. A theoretical framework of ecological phase transitions for characterizing
treegrass dynamics. Acta Biotheoretica 50: 141–154.
Limburg, K.E., O’Neill, R.V., Costanza, R., Farber, S., 2002. Complex systems and
valuation. Ecol Econ 41: 409–420.
Liu, S., Costanza, R., Farber, S., Troy, A. 2010. Valuing ecosystem services. Theory,
practice, and the need for a transdisciplinary synthesis. Ann. N.Y. Acad. Sci. 1185: 54–78.
Millennium Ecosystem Assessment (MEA), 2003. Island Press, Washington DC.
Millennium Ecosystem Assessment (MEA), 2005. Island Press, Washington DC.
Müller, F., Jones, K.B., Krauze, K., Li, B.-L., Victorov, S., Petrosillo, I., Zurlini, G., Kepner,
W.G., 2008. Contributions of landscape sciences to the development of environmental
security. In: Petrosillo, I., Müller, F., Jones, K.B., Zurlini, G., Krauze, K., Victorov, S., Li, B.L., Kepner, W.G. (Eds.), Use of Landscape Sciences for the Assessment of Environmental
Security. Springer, Dordrecht, The Netherlands, pp. 1–17.
Nelson, E., Mendoza, G., Regetz, J., Polasky S., Tallis, H.D., Cameron, R., Chan, K.M.,
Daily, G.C., Goldstein, J.P., Kareiva, M., Lonsdorf , E., Naidoo, R., Ricketts, T.H., Shaw,
M.R., 2009. Modeling multiple ecosystem services, biodiversity conservation, commodity
production, and tradeoffs at landscape scales. Front. Ecol .Environ. 7(1): 4–11.
Noss, R. F. 1987. From plant communities to landscapes in conservation inventories: a
look at the Nature Conservancy (USA). Biol. Conserv. 41:11-37.
O’Neill, RV., Kahn, J.R., 2000. Homo economicus as a keystone species. Bioscience 50:
333–337.
Ostfeld, R.S., LoGiudice, K., 2003. Community disassembly, biodiversity loss, and the
erosion of an ecosystem service. Ecology, 84: 1421–1427.
Peterjohn, W. T., Correll, D. L., 1984. Nutrient dynamics in an agricultural watershed.
Observations on the roles of a riparian forest. Ecology 65:1466-1475.
Petrosillo, I., Zaccarelli, N., Zurlini, G., 2010. Multiscale vulnerability of natural capital in a
panarchy of social-ecological landscapes. Ecol. Complexity , in press doi:
10.1016/j.ecocom.2010.01.001
Pohl, M., Alig, D., Körner, C., Rixen, C., 2009. Higher plant diversity enhances soil
stability in disturbed alpine ecosystems. Plant Soil 324:91–102.
Ricketts, T.H., Daily, G.C., Ehrlich, P.R., et al. 2004. Economic value of tropical forest to
coffee production. P Natl Acad Sci USA 101:12579–82.
Ricketts, T.H., Regetz, J., Steffan-Dewenter, I, Cunningham, S.A., Kremen, C.,
Bogdanski, A., Gemmill-Herren, B., Greenleaf, S.S., Klein, A.M., Mayfield, M.M., Morandin,
L.A., Ochieng, A., Viana B.F., 2008. Landscape effects on crop pollination services: are
there general patterns? Ecology Letters 11: 499–515.
Sala, O.E, Vuuren, D.V., Pereira, H.M., et al. 2005. Biodiversity across scenarios. In:
Carpenter SR, Pingali PL, Bennett EM, et al. (Eds). Ecosystems and human well-being:
vol 2. Scenarios: findings of the Scenarios Working Group. Washington, DC: Island Press.
Schmid, B., Joshi, J., Sclapfer, F., 2001. Empirical evidence for biodiversity-ecosystem
functioning relationships. In: The Functional Consequences of Biodiversity: Empirical
Progress and Theoretical Extensions (eds Kinzig, A.P., Pacala, S.W. & Tilman, D.).
Princeton University Press, Princeton, pp. 120–150.
Schwartz, M.W., Brigham, C.A., Hoeksema, J.D., Lyons, K.G., Mills, M.H., van Mantgem,
P.J. (2000). Linking biodiversity to ecosystem function: implications for conservation
ecology. Oecologia, 122: 297–305.
Steffen, W., Sanderson, A., Tyson, P., Jäger, J. 2004. Global Change and the Earth
System: A Planet Under Pressure. IGBP Global Change Series. Springer-Verlag, New
York, 332 pp.
Solan, M., Cardinale, B.J., Downing, A.L., Engelhardt, K.A.M., Ruesink, J.L., Srivastava,
D.S., 2004. Extinction and ecosystem function in the marine benthos. Science, 306: 1177–
1180.
Symstad, A.J., Chapin, F.S., Wall, D.H., Gross, K.L., Huenneke, L.F., Mittelbach, G.G. et
al. (2003). Long-term and large-scale perspectives on the relationship between biodiversity
and ecosystem functioning. Bioscience 53: 89–98.
Thomson, J.D., Goodell, K., 2001. Pollen removal and deposition by honeybee and
bumblebee visitors to apple and almond flowers. J. Appl. Ecol., 38: 1032–1044.
Tilman, D., Cassman, K.G., Matson, P.A., Naylor, R., Polasky, S., 2002. Agricultural
sustainability and intensive production practices. Nature 418: 671–677.
Troy, A., Wilson, M.A., 2006. Mapping ecosystem services: practical challenges and
opportunities in linking GIS and value transfer. Ecol Econ 60: 435–49.
Turner, M.G., Gardner, R.H., O’Neill, R.V., 2001. Landscape Ecology in Theory and
Practice. Springer-Verlag, NY.
Turner, W.R., Brandon, T., Brooks, M., et al. 2007. Global conservation of biodiversity and
ecosystem services. BioScience 57: 868–73.
Vitousek, P.M., Mooney, H.A., Lubchenco, J., Milillo, J.M., 1997. Human domination of
earth’s ecosystems. Science 277: 494–499.
Walker, B., J.A. Meyers, 2004. Thresholds in ecological and social–ecological systems: a
developing database, Ecol. Soc. 9 (2), 3. Online URL:
http://www.ecologyandsociety.org/vol9/iss2/art3/
Wiens, J.A. 1989. Spatial scaling in ecology. Funct. Ecol. 3: 385–397.
Wiens, J.A., Milne, B.T., 1989. Scaling of ‘landscapes’ in landscape ecology, or, landscape
ecology from a beetle’s perspective. Landscape Ecol 3: 87–96.
With, K.A., 2004. Assessing the risk of invasive spread in fragmented landscapes. Risk
Anal. 24 (4): 803–815.
Zaccarelli, N., Petrosillo, I., Zurlini, G., Riitters, K.H., 2008. Source/sink patterns of
disturbance and cross-scale effects in a panarchy of social-ecological landscapes. Ecology
& Society 13(1): 26. [online] URL: http://www.ecologyandsociety.org/vol13/iss1/art26/
Zavaleta, E.S., Hulvey, K.B., 2004. Realistic species losses disproportionately reduce
grassland resistance to biological invaders. Science, 306: 1175–1177.
Zurlini, G., Riitters, K. H., Zaccarelli, N., Petrosillo, I., 2007. Patterns of disturbance at
multiple scales in real and simulated landscapes. Landscape Ecol. 22:705-721.
Table 1. The four different classes of ecosystem services defined by the Millennium
Ecosystem Assessment (MA 2005)
Provisioning: products obtained from ecosystems (e.g. food, fiber, fresh water)
Regulating: benefits obtained from the regulation of ecosystem processes (e.g. air quality,
climate and water regulation)
Cultural: nonmaterial benefits people obtain from ecosystems (e.g. spiritual enrichment,
recreation)
Supporting: services necessary for the production of other ecosystem services (e.g.
nutrient and water cycling, photosynthesis)
Table 2. Ecosystem services, classified according to the Millennium Ecosystem
Assessment (MEA 2005), and their direct and intermediate ecosystem service providers.
Functional units refer to the unit of study for assessing functional contributions of
ecosystem service providers; spatial scale indicates the scale(s) of operation of the
service (Petrosillo et al. 2010; modified from Kremen, 2005)
Service
Direct and intermediate
ecosystem service providers
(ESPs) /organisation level
Aesthetic, cultural
All biodiversity, landscape land use/cover
Ecosystem goods
Diverse species, supporting landscape land
use/cover
Biogeochemical cycles, micro-organisms,
supporting landscape, land use/cover
Micro-organisms, plants, landscape, land
use/cover
UV protection
Purification of air
Flood mitigation
Drought mitigation
Climate stability
Pollination
Landscape, land use/land cover
Landscape, land use/land cover
Landscape, land use/land cover
Insects, birds, mammals and supporting
landscape, land use/land cover
Pest control
Invertebrate parasitoids and predators and
vertebrate predators and supporting
landscape, land use/cover
Landscape, land use/cover, soil microorganisms, aquatic micro-organisms,
aquatic invertebrates and supporting
landscape, land use/cover
Leaf litter and soil invertebrates; soil
micro-organisms; aquatic micro-organisms
and supporting landscape, land use/cover
Leaf litter and soil invertebrates; soil
micro-organisms; nitrogen-fixing plants;
plant and animal production of waste
products and supporting landscape, land
use/cover
Ants, birds, mammals and supporting
landscape, land use/cover
Purification of water
Detoxification and
decomposition of
wastes
Soil generation and
soil fertility
Seed dispersal
Disturbance regulation
(includes human
disturbances, flood,
drought, invasive
species, pest)
Landscape, land use/cover, supported
parasitoids and vertebrate predators
Functional units
Spatial scale
Species, populations,
communities, habitats, landscapes
Species, populations,
communities, habitats, landscapes
Biogeochemical cycles,
functional groups, landscape
Biogeochemical cycles,
populations, species, functional
groups
Communities, habitats, landscape
Communities, habitats,landscape
Communities, habitats, landscape
Species, populations, functional
groups, communities, habitats,
landscapes
Species, populations, functional
groups, communities, habitats,
landscapes
Species, populations, functional
groups, communities, habitats,
landscapes
Local–global
Species, populations, functional
groups, communities, habitats,
landscapes
Species, populations, functional
groups, communities, habitats,
landscapes
Local–regional
Species, populations, functional
groups, communities, habitats,
landscapes
Species, populations, functional
groups, communities, habitats,
landscapes
Local
Local–global
Global
Regional–global
Local–regional
Local–regional
Local–global
Local
Local-regional
Local–regional
Local
Local-regional
Table 3. Ecosystem services classified according to their spatial characteristics (from
Costanza 2008)
1. Global non-proximal (does not depend on proximity)
1&2. Climate regulation
Carbon sequestration (NEP)
Carbon storage
17. Cultural/existence value
2. Local proximal (depends on proximity)
3. Disturbance regulation/ storm protection
9. Waste treatment
10. Pollination
11. Biological control
12. Habitat/refugia
3. Directional flow related: flow from point of production to point of use
4. Water regulation/flood protection
5. Water supply
6. Sediment regulation/erosion control
8. Nutrient regulation
4. In situ (point of use)
7. Soil formation
13. Food production/non-timber forest products
14. Raw materials
5. User movement related: flow of people to unique natural features
15. Genetic resources
16. Recreation potential
17. Cultural/aesthetic
Table 4: Some examples of ecosystem service efficiency, which is a measure of
effectiveness at performing the service, for different ecosystem services from the literature;
services are classified as regulating or supporting according to the Millennium Ecosystem
Assessment (MEA 2003) (modified from Kremen 2005).
Service
classification
Regulating
Service
Carbon storage
Regulating
Crop pollination
Regulating
Crop pollination
Regulating
Disease control
Regulating
Regulating
Leaf litter
decomposition in
streams
Pest control
Regulating
Dung burial
Regulating
Water flow
regulation
Invasion resi
stance
Regulating
Regulating
Regulating
Regulation
Supporting
Soil stability on
mountain slopes
Disturbance
regulation (as
LULC change
over time)
Coastal
protection, wave
attenuation,
habitat refugia,
nursery
Bioturbation
Supporting
Nutrient cycling,
mineralization
Supporting
Above-ground
net primary
productivity
Mineralization
and
decomposition
Monthly LULC
Supporting
Supporting
Ecosystem service
provider
Tree species (per
capita)
Bee species (per
capita) or community
Bee species (per
capita)
Vertebrate host
species (per capita)
Stream invertebrate
species (per capita)
Efficiency measure(s)
Biomass accumulation
rate
Pollen deposition per
visit; seed or fruit set
with and without bees
Ratio of pollen
deposition to removal
Disease dilution rate
Leaf-shedding process
rate
Insect parasitoid
species (per capita)
Dung beetle species
(per capita)
Forest habitats
Parasitism rate
Examples
(reference)
Balvanera et al.
(2005)
Kremen et al.
(2002)
Thomson and
Goodell (2001)
Ostfeld and
LoGiudice (2003)
Jonsson et al.
(2002)
Burial rate
Kruess and
Tscharntke (1994)
Larsen et al. (2005)
Water flow rate
Guo et al. (2000)
Herbaceous
community (native
plus naturalized
species)
Herbaceous species
Invader biomass m-2 ;
change in resident
biomass/unit invader
Zavaleta and
Hulvey (2004)
Species diversity
Pohl et al. (2009)
Perennial cultivations
(olive groves and
vineyards)
Pattern (composition
and configuration) at
multiple scales
Zurlini et al. (2007);
Zaccarelli et al.
(2008)
Mangrove forests,
seagrass beds, and
coral reefs
Density of plants,
sedentary of animal
material, and
bathymetry
Koch et al. (2009)
Benthic marine
invertebrate species
(per capita)
Soil microbial
community/functional
groups
Herbaceous
community
Bioturbation potential
index
Solan et al. (2004)
Process rates
Balser et al. (2001)
Biomass accumulation
rate
Herbaceous
community
N leaching or retention;
decomposition rate;
microbial biomass, etc.
Normalized Difference
Reviewed in
Schmid et al.
(2001)
Reviewed in
Schmid et al.
(2001)
Zaccarelli et al.
Different land uses
photosynthetic
activity
and land covers
Vegetation Index
(NDVI) from remote
sensing
(2008)