Identifying spatial priorities for protecting ecosystem services

Introduction

Ecosystem services (ES) are vital for human well-being¹. Much attention has been devoted to mapping and quantifying ES to achieve the dual goals of protecting biodiversity and human well-being. A growing number of broad-scale mapping studies aim to identify priority regions for conducting more localised place-based management of ES [e.g.,^2–5]. Place-based management requires intensive collection of detailed socio-economic and biophysical data, and close collaboration with stakeholders for effective decision making^6,7. Given limited resources and information, and increasing threats to ecosystems, it is not possible to do these comprehensive analyses everywhere in a timely manner. We argue that there is currently an under-appreciated, but vital role for spatial prioritization of locations in which place-based management should occur so that attention is focussed on those locations where resource investment will yield the greatest return for human well-being. Indeed, data are deficient in most locations for informing comprehensive and accurate analyses of trade-offs in ES management, and spatial prioritization is a crucial precursor to attempting such trade-off analyses so that data mining efforts occur in the most critical locations. Moreover, prioritization is essential because much ES management is conducted by government or non-government organizations (NGOs) that could potentially operate in many places.

Given the important role that broad-scale prioritization can play in guiding decisions about where to conduct place-based ES management, a critical assessment of current prioritization approaches is warranted. Some schemes for identifying spatial priorities for managing ES are simple characterizations of biophysical processes and social demand, with little consideration of important information such as the availability of alternatives to ES for meeting human needs, threats to service provision, and the costs of management actions. Although fundamentally different to spatial prioritization for biodiversity conservation, spatial prioritization of ES may be guided by some of the key principles of the former. Spatial prioritization for conservation is well established and may be applied at coarse (e.g., biodiversity hotspots or priority ecoregions;⁸) or fine scales, identifying locations or actions in locations that are relatively more important for protecting biodiversity than other actions or other locations⁹. As with spatial prioritization of ES, spatial prioritization for conservation may help to identify locations where more detailed systematic conservation planning should be conducted, and is just one component of the planning process^10,11.

Spatial prioritization of ES differs from spatial prioritization for conservation because ES are valued primarily for their worth to humans, can be transferable across space (may not need to be protected at a specific location), are sometimes substitutable by human engineering, and service beneficiaries define the success of management actions. Yet, as with spatial prioritization for conservation, spatial prioritization of ES can guide decsions about local-scale planning and inform the allocation of resources from management agencies (e.g., World Wildlife Fund;¹²). Moreover, spatial prioritization for conservation is a useful starting framework for ES prioritization because the former is well entrenched in planning discourse¹³ and yields valuable lessons for ES management¹⁴.

Current approaches to identifying spatial priorities for managing ES apply different prioritization methods (see Table 1), and developing more consistent and comprehensive methods is an important goal for future prioritization studies. We review past approaches to spatial prioritization of ES, identifying key aspects that should be considered in future analyses. At appropriate places we discuss the relevance of spatial prioritization for biodiversity conservation to spatial prioritization of ES because certain aspects, such as accounting for costs and threats, are common to both. We then demonstrate the importance of these aspects through a conceptual framework for prioritization that outlines an approach for managing the most vital ES for the least cost where they are most needed¹⁵. We illustrate the framework with a worked example using the ES of water provision. Egoh et al.¹⁴ reviewed the extent to which ES were included in conservation assessments (≈ identifying spatial priorities). Our work differs from Egoh et al. by assessing how ES priorities have been identified and how methods for prioritization should be improved. It also complements discussions of other aspects of ES management such as how to operationalize ES on the ground¹⁶, developing appropriate payments for services schemes (e.g.,^17,18) or how to manage service provision at specific sites [e.g.,^19,20].

Components of spatial prioritization

The following are key elements to any conservation prioritization problem: biodiversity features [assets] that need protection (e.g., species or habitats); processes that threaten these features (e.g., habitat loss); a set of actions that may be effective at abating the threats (e.g., manage invasive species); and financial information specifying the cost of implementing each action, and the available conservation budget¹¹. ES prioritization shares these elements; that is, identifying ecosystem features that supply services, threats to service provision, potential actions to ensure future supply of services, and the costs of these actions. Yet, prioritization of services requires at least the following additional considerations: the availability of alternative means of providing benefits supplied by services; the capacity of an ES to meet human demands; and scale of, and site dependency in, the delivery of services.

While each of these factors may contribute to the economic valuation of an ES (i.e., captured by a metric such as dollar value) such complete and site-specific economic values are rare. Studies that estimate the financial value of ES facilitate the appreciation of services in widely understood terms, but this approach has well recognised limitations including the fact that financial values under-represent benefits to the poor as they have less capacity to pay than rich people^21–23. Therefore, it is important to explore alternative approaches to identifying spatial priorities for ES management that circumvent some of the limitations of using financial values.

An example of spatial prioritization

The relationships among the various components of our conceptual framework for spatial prioritization of ES are presented in Figure 1. We illustrate our approach in this section using a worked example based on data published in Luck et al.¹⁵ focussing, for the sake of simplicity, on a single ES: water provision.

Figure 1. Key aspects for consideration in ecosystem-service prioritization.

The global analysis of Luck et al.¹⁵ identified watersheds that are a priority for protecting particular ES. The first step in the analysis was to quantify the benefits and supply of the service. The benefits of protecting the supply of potable water was measured through human population density in each watershed; that is, there were greater benefits to protecting supply in watersheds with higher population density compared to those with lower density. Water supply was measured using a global hydrological model, and ‘water-production efficiency’ was calculated for each watershed by dividing supply in each watershed with watershed area.

The costs of actions to manage water provision were represented using a proxy for resource (e.g., land acquisition and infrastructure) and maintenance (e.g., labour) costs. This proxy incorporated data on total income in the watershed (per capita gross national income), population size and watershed area. Resource costs were assumed to scale positively with per-capita wealth and population density (assuming that land and infrastructure prices are generally higher where population density is higher), while maintenance costs were assumed to also scale positively with per-capita wealth. Finally, the cost-effectiveness of protecting the service in each watershed was calculated by dividing human population density and water supply (benefits) by the cost.

The capacity to meet demand was measured using values for water supply and water withdrawals in each watershed. It also considered regional water deficits (withdrawals > supply) and the proportion of total supply that remained once demands were met, adjusting the watershed-level capacity measure downwards proportional to the need to move water to regions (within a watershed) where supply did not meet demand. It was assumed that managing the service of water provision was most important in watersheds where supply barely meets or is short of demand, and less important when supply greatly exceeded demand.

To estimate threat to water provision, expected vegetation cover in each watershed was used, recognising the link between vegetation and water provision, filtration and the maintenance of water quality (although this link is decidedly complex; see Luck et al.¹⁵ for details). Vegetation cover and type in a watershed may be indicative of the capacity of the watershed to provide potable water naturally, and change in vegetation cover can be considered a proxy for threat to water provision. To quantify this threat, the following data were used: the proportion of each watershed covered in tree, shrub and herbaceous vegetation; the annual rate of change in vegetation cover (over a proceeding 5-year period); the time span over which change in cover would be predicted (e.g., 20 years); and the proportion of the watershed that was protected (assuming vegetation in protected areas could not be cleared). Watersheds with mid-range values of vegetation cover, rates of vegetation loss and/or area protected were considered priorities for water provision management, because, for example, watersheds with low cover and high rates of loss would require large investments in ES management relative to return, whereas watersheds with high cover and low rates of loss are under less threat to the disruption of the service.

The final consideration in spatial prioritization is the availability of alternatives to the provision of the service via ecosystems. Improvements in the supply of potable water may be made through the construction of dams and building of filtration plants, for example, rather than ecosystem management. The availability of these alternatives is often a function of the capacity of local communities to pay for them, in addition to other constraints (e.g., topographic suitability for dam construction). Therefore, Luck et al.¹⁵ used the gross national income per capita of countries spanning each watershed as an indicator of the capacity of communities reliant on the watershed to pay for alternatives to natural water provision.

The above components were combined into a single index representing the relative importance of each watershed for protecting water supply. This example, and our prioritization framework generally, is appropriate when planning units are large and there are a variety of available options for managing services, and it is difficult to express the components of prioritization precisely. Our framework treats the supply of services, threats and costs in ES prioritization inclusive of beneficiary demand, capacity to meet demand, and availability of alternatives to ES provision.

Discussion

Our review of current approaches to identifying spatial priorities for managing ES found that the important components of prioritization (benefits, costs, threats, availability of alternative, and capacity to meet demand) were treated in substantially different ways or sometimes omitted completely (although not all components are applicable in every context). Moreover, few studies explicitly addressed the issue of site dependency and scale in the provision of services and/or location of beneficiaries. Accordingly, there is substantial scope for improving ES analyses aimed at identifying spatial priorities for managing services.

If ES benefits were commodities in perfect markets, the price of such benefits would reflect all of the spatial prioritization components identified above. Yet, many ES benefits are not commodities, and for those that are, the associated markets are far from perfect, suffering from numerous market failures including monopsony (single buyers, as in reverse auctions), oligopoly (few sellers, as in many payments-for-ES schemes), externalities and information asymmetries. This means that market prices will not generally reflect all of the components of prioritization appropriately. Stated-preference non-market valuation approaches can be informative in certain settings where markets do not apply, but they are generally of limited utility reflecting the various dimensions of value/social priority^44,45. Accordingly, even where economic valuation data are available, it will still be appropriate for ES prioritization exercises to separately integrate some of the components we identify.

Although we have focussed on the mechanics of prioritization, the following issues must be addressed prior to such analyses: 1) identification of the ES to be included; 2) capacity to access spatially explicit data; and 3) data quality (Figure 1). Identifying important ES should occur through in-depth consultation among scientists, policy-makers, managers and stakeholders (especially service beneficiaries; see Fisher et al.⁴⁶). For example, if prioritization was required across a particular country, federal management agencies and relevant stakeholders may engage in a process of identifying those services most important to the well-being of the country. ‘Importance’ may be a factor of the total financial value of a service (e.g., agricultural production) and/or the societal need for a service (e.g., provision of potable water) and assessed through appropriate valuation approaches. Hence, a priority list of which services to focus on is required prior to deciding on where to invest in ES management. The selection of different services will influence the approach to spatial prioritization because ES management will have different objectives (e.g., improving timber harvest or maintaining water supply). However, the major steps we outline here will be relevant in most cases.

Application of our approach requires spatially explicit data on where services are produced, benefits, costs and threats. Data on spatially explicit production is usually obtained through mapping the location of ecosystems that provide services (e.g., grasslands that support livestock). Mostly, this involves maps of vegetation types or water sources (Table 1). Benefits are represented spatially generally via the biophysical quantity of a given service produced by a given location (e.g., carbon stored) and/or its financial value. To represent costs spatially, researchers have used simply the area of the planning unit (e.g.,³⁹, for some services) or current land values [e.g.,³⁴]. Documenting costs is problematic because of spatio-temporal variation in financial values, which is possibly why some researchers have resorted to more simple rules-of-thumb (e.g., assuming that managing larger areas yields greater costs). Also problematic is spatially explicit measures of threat, which have been represented by, for example, maps of land-use or historical or potential land-cover change and how these relate, spatially, to the location of service provision [e.g.,^30,47] (Table 1).

Finally, the type of data used in prioritization will greatly affect outcomes. For example, Anderson et al.² demonstrated that variation in the resolution (≈ grain size) and/or spatial extent of a prioritization analysis influenced the level of congruence between biodiversity and ES priorities. Moreover, data quality may be poor for certain services and certain components of prioritization. For example, crude proxies or indicators may be required for services for which it is difficult to obtain accurate, spatially explicit measures of supply and demand (e.g., flood mitigation; see Holland et al.⁴⁸). Our framework, which promotes the use also of data on threats, costs, alternatives and site dependency, may help to alleviate this issue because prioritization could be based just on those components for which data quality is acceptable.

The most appropriate metric to represent the supply of the service will be context dependent, but the use of biophysical quantities will be suitable in most cases. For example, if the service is storm protection then a suitable metric may be the area of mangroves that needs to be maintained to deliver a given level of protection [e.g.,²⁵]. ES supply should be assessed relative to the demand for the service, which can be measured using a target-based approach, through current or projected use of the service or its products, through demonstrated need for the service (e.g., historical impacts of storms) or through meeting an accepted minimum standard (e.g., acceptable losses due to storm damage). Quantifying demand for a service requires the implicit or, preferably, explicit identification of beneficiaries, which may be immediate beneficiaries (e.g., residents of coastal villages threatened by storms) and/or ‘non-immediate’ beneficiaries (e.g., consumers of goods produced by the villages).

The application of prioritization frameworks generally involves multiple services across many planning units and priorities for different services are not necessarily congruent². This requires an analysis of trade-offs between services in managing land/sea-space for service provision⁵. Moilanen et al.⁴⁹ addressed this issue using a multi-objective prioritization approach for biodiversity and ES based on the conservation planning software Zonation. The authors argued that regional variation in land-use priorities meant that spatial separation of land uses may reduce management conflicts. Moreover, areas that are a priority for multiple ES could be used in trade-offs assuming the areas were not critical priorities for every service.

In multi-objective prioritization frameworks that consider spatial separation of ES management or trade-offs in land-use priorities, it is vital to address site-dependency and scale of service provision and location of beneficiaries. As we argue above, there is little flexibility in managing for the provision of services that are delivered locally to in situ beneficiaries (e.g., flood mitigation). Avoiding inappropriate management decisions and trade-offs rests entirely on taking a comprehensive approach to identifying priorities. Here we describe the major factors that must be considered in ES prioritization and argue that addressing as many of these factors as possible will improve the outcomes of multi-objective prioritization frameworks that aim to promote human well-being through the protection of services.

Developing comprehensive methods for identifying ES priorities is much more than just an academic exercise. Governments and NGOs across the world are increasingly including the protection of ES into their policy directives. For example, the governments of China, Costa Rica and Mexico pay landholders that engage in management that protects the supply of hydrological services^50–52. Vital to this process is identifying locations that offer the greatest return on investment. This requires a systematic and thorough approach to identifying spatial priorities for protecting ES.