Conservation Planning of Multiple Ecosystem Services in the Yangtze River Basin by Quantifying Trade-Offs and Synergies

Ecosystem services offer various direct or indirect positive benefits of nature to people, which are categorized by the Millennium Ecosystem Assessment (MEM) as provisioning, regulating, cultural, and supporting services [1]. Ecosystem services are increasingly being destroyed and threatened due to the utilitarian consumption of natural resources [2,3]. Biodiversity has long been the uppermost concern in conservation planning, and over the last decade or two [4,5,6,7], high-value ecosystem service areas have become of great importance [8,9,10]. Researchers have called for ecosystem services to be included in conservation planning [11,12,13,14]. The Kunming–Montreal Global Biodiversity Framework also reached an agreement stating that a target of no less than 30% of the world’s terrestrial, freshwater and marine areas, especially areas of significant value in relation to their biodiversity and ecosystem services should be maintained with effective conservation and management (22 December 2022, https://www.un.org/).

Conservation planning uses customized rules to identify optimal geographic areas of conservation features with significant natural resource value. The efficacy of conservation planning lies in achieving conservation goals under restricted conditions [15,16]. The fundamental mathematical theory of conservation planning is tackling the problem of maximal coverage or the minimal set problem, which is to cover all elements of conservation while keeping the cost as low as possible [17,18]. Several popular conservation planning theories have been put forward since the early 1880s [19,20]. One representative method is biogeographic regionalization, which divides the regions into similar or identical units by topography, hydrology, temperature, soil, etc. [21]. On the other hand, the hotspot method tries to protect the areas with high species richness, which raises the probability of species with maximal element cooccurrence being included [22]. With few exceptions, the commonality of these methods exclusively counts on their consideration of high-value areas, where the species are either high in amounts or exhibit high diversity. Though most valuable regions are included in the final planning, some rare or endemic species might be missed [23,24]. To compensate for these disadvantages, systematic conservation planning (SCP) carries out the planning with the principles of representativeness, complementarity, and persistence, providing a foundation to achieve conservation goals comprehensively and efficiently [15]. Widespread successful practices have been implemented using SCP to look for additional areas from existing reserves of networks or to design brand-new portfolios of protected areas.

Conserving ecosystem services is not fully the same as preserving biodiversity. Ecosystem services have two unique characteristics that are different from biodiversity. One is that the measured value for an ecosystem service is a continuous variable, while the estimated value for biodiversity is a discrete variable. Traditional biodiversity goals are a count of the number of species, while ecosystem service values can be subdivided. Ecosystem services lack movability and consecutive distributions compared to biodiversity. Another factor is that interactions with trade-offs and synergies exist among different ecosystem services [25,26,27]. Preserving one ecosystem service would be at the expense of disregarding the others [28]. Likewise, exploring the correlations between habitat quality (biodiversity) and other ecosystem services is one of the focus points of existing research [29,30,31], but opinions are divergent regarding their spatial congruence, and the correlations have been found to be both positive and negative [17,32,33]. Given this case, striving to find correlations rather than to quantify and optimize ecosystem services with SCP would be harsh or meaningless. One reason for this divergence is that most studies capture the relationships of ecosystem services with the total value of the entire study area [34,35]. Also, spatial heterogeneity is apparent in ecosystem services, and conclusions may vary with different scales. Explicit calculation of the spatial correlation among ecosystem services is an essential prerequisite for figuring out conservation plans that consider trade-offs and synergies.

Numerous studies have been conducted on the trade-offs and synergies of ecosystem services. However, they are predominantly interested in the relationship of the total amount of each ecosystem service, the driving factors, dynamic spatial and temporal changes in ecosystem services, and investigating higher-priority areas to be protected; the distributions of the selected areas are scarcely discussed [42,43,44,45]. To raise the accuracy of the trade-off analysis, we used MaxEnt to evaluate the suitability of biodiversity, and the NDVI, NPP, and precipitation as an adjustment layer to reflect the heterogeneity of the same land-use/land-cover type, which is a crucial factor in determining ecosystem services themselves. Similarly, ecosystem service value estimation models such as InVEST and CASA employ topographical, metrological, or soil-type data as correction factors. Even though this evaluation can be limited by the accessibility, scale, and time-sequential nature of the data, the values of the same land-use/land-cover type over different years are commonly homologous, which means that as long as the land is not transformed, the ecosystem service values would not be changed [46,47]. In addition, we used the Local Moran’s I to seek the relationship among ecosystem services, which is widely used in the field of economics [48]. Areas adjacent to the target areas also contributed to the calculation, which allowed us to explore the relationships of ecosystem services more accurately on a local scale. The spatial autocorrelation of the paired target areas was captured as clusters, and they were classified into five kinds. Ignoring the spatial proximity when calculating the correlations between ecosystem service values would mean each calculated area shares its maximum or minimum values with another certain region, leading to underestimation or overestimation of its trade-offs and synergies.

MARXAN, as a decision support tool, is mainly and extensively used in creating protected area systems aimed at conserving biodiversity or biodiversity surrogates [41], including character or trait diversity, species diversity, species assemblage, landscape pattern, and habitat diversity. SCP has been proven to be the best practice in biodiversity conservation. A few studies have tried to use MARXAN to make ecosystem service conservation planning; nonetheless, they either broke the study area into zones to develop corresponding management strategies [49] or simply aimed to maximize the benefits of protecting ecosystem services in their planning [50]. These all failed to consider the complementarity and spatial correlations of different ecosystem services in systematic conservation planning. Commonly, areas with the highest ecosystem service values should be selected. A typical and well-known area selection method that follows this concept is the hotspot method. Hotspots focus on choosing richness or range-size rarity in the species of the areas. Case studies show that the method of making rules from the perspective of conserving the biologically rich spots instead of the whole species will lead to some species being excluded from all of the species of the region in the final solution when the target level is not high, and more areas and economic cost would thus be needed.

Further, our statistical analysis of the ecosystem service value distribution showed that comprehensive planning for all of the ecosystem services was more efficient than optimizing each one alone. When the target level for conservation was raised, though the percentages of planning units to be included for both optimizing one ecosystem service alone or all ecosystem services were also increased, the planning units at a high-value level were not the best choices in the scenario of optimizing for all of the ecosystem services. When conserving 20% of all ecosystem services, the percentage of planning units at value levels 6 and 7 was obviously lower than that of the planning unit when optimizing HQ, CS, and HR alone. This implicated that there were trade-offs among the HQ, CS, and HR in their high-value areas. The Local Moran’s I cluster maps of the three paired ecosystem services also showed that H-H agglomerations were consistent in their value distributions, and these were in the lower reaches of the YRB. For EP, there was not much difference in the percentage of the planning units between optimizing just the EP and optimizing all of the ecosystem services. This indicated that the distributions of high-value areas for EP and the three other ecosystem services were synergetic. Hence, aiming at a single objective merely counting on the high-value areas was more efficient than considering the co-benefit areas with other ecosystem services [51], as it achieved the goal with less areas. It was obvious that when only concentrating on a single ecosystem service in low target levels, solitary high-value areas were selected merely because they had high values, thus achieving the goal faster regardless of their contribution to the whole region and enabling subsequent sustainable management.

In addition, it should be noted that there is complementarity among ecosystem services when developing optimal strategies to enhance them. The correlation matrix between ecosystem services can be viewed as their complementarity: the higher its value, the higher the complementarity between the two ecosystem services. This indicates that more areas would be needed to conserve both of the two ecosystem services. Moreover, areas with high complementarity will not necessarily be the high-value ones. For example, the JSD of HR and EP was 0.29, and the percentage values were nearly the same when optimizing each, but this differed when optimizing all of the ecosystem services. More areas at low-value levels were increasingly needed for achieving the goals. It is indisputable that if we concentrate on these seemingly valuable areas with single-objective planning, more resources would be consumed in the face of the new requirements. The selection of priority areas for all of the objectives should be handled systematically. The Chinese government has been promoting a “multi-planning integration” reform since 2014 (https://www.mnr.gov.cn), the core notion of which is to include multiple planning considerations in a single plan, such as considering national economy and social development planning, town and country planning, land planning, etc., to ensure that all of the planning can maintain inherent consistency across areas. Our methodology of analyzing the trade-offs and synergies of multiple ecosystem services could be used to investigate the cohesion mechanism of multi-planning. The advantages and disadvantages of different plans could be compared by means of the statistical analysis of ecosystem service value distribution. Lastly, our integrated conservation planning method for all ecosystem services is helpful in providing enlightenment for drawing up multiple plans on the same map to reconcile their benefits and conflicts.

Our study still has some limitations. Although we compared the trade-offs and synergies among ecosystem services, other correlations were not analyzed, such as the demand and flow [52,53,54]. The beneficiaries of ecosystem services are people and nature, and efficiency will go a step further if the flow paths between the demand areas and supply areas are identified, causing extra losses and costs in long-distance transmission [55]. Moreover, we did not incorporate restoration planning; ecological restoration projects could restore high-value ecosystem service areas, and the Chinese government are also implementing policies of ecological protection and restoration for mountains/rivers/forests/farmlands/lakes/grasslands. From the view of this prospect, our study proposed that governments, stakeholders, and managers should be involved together to develop strategies of conservation and restoration, and the trade-offs involved in meeting their goals are crucial in systematic planning. A comprehensive planning approach is more sustainable than piecing together multiple separate plans.