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Article

When to Use What: A Comparison of Three Approaches to Quantify Relationships Among Ecosystem Services

by
Zhen Zhong
1,†,
Bochuan Zhou
2,†,
Lingqiang Kong
3 and
Xuening Fang
1,4,*
1
School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
2
School of Finance and Business, Shanghai Normal University, Shanghai 200234, China
3
School of Public Administration, Zhejiang University of Finance and Economics, Hangzhou 310018, China
4
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, Shanghai 200234, China
*
Author to whom correspondence should be addressed.
†
Co-first author of this work.
Land 2025, 14(3), 644; https://doi.org/10.3390/land14030644
Submission received: 11 February 2025 / Revised: 11 March 2025 / Accepted: 16 March 2025 / Published: 18 March 2025
(This article belongs to the Special Issue Spatial-Temporal Evolution Analysis of Land Use)
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Abstract

:
Sustainable landscape management requires accurately identifying the trade-offs and synergies among ecosystem services (ES). Three commonly utilized approaches to quantify ES trade-off/synergy relationships include the space-for-time approach, landscape background-adjusted space-for-time approach, and temporal trend approach. However, the similarities and differences among these three approaches in identifying ES relationships in the same area remain unclear. Thus, we conducted a case study in the rapidly urbanizing Yangtze River Delta region, comparing the three approaches based on annual data spanning from 2001 to 2020 for 12 types of ES. We found that: (1) the ES trade-off/synergy relationships detected by the three approaches exhibit significant divergence, with only 1.45% consistency among the 66 pairs of ES relationships. (2) All three approaches can overlook ES trade-offs, miss ES synergies, and erroneously detect interactions where none exist. (3) The mechanisms contributing to the misidentification of ES relationships by the three approaches include: neglecting the underlying assumptions of different approaches, insufficient time interval length, short time series of ES data, data aggregation effects, non-linear changes in ESs, time lag effects of ES relationships, among others. Our results indicate that each of the three approaches has its own advantages and disadvantages in identifying ES relationships. Prior to selecting an approach for identifying relationships between ESs in a specific study area, careful consideration of the availability of time series data, the characteristics of the chosen ES type, and thorough examination of the underlying assumptions and uncertainties of each approach are imperative.

1. Introduction

Sustainable development requires landscapes to simultaneously provide multiple ecosystem services (ESs) to enhance human well-being [1,2,3,4,5,6]. However, different types of ES are not isolated but rather exhibit complex relationships with each other [7]. On one hand, there are “synergistic” relationships among ESs, where an increase in one ES leads to growth in several other ESs. On the other hand, there are “trade-off” relationships, where an increase in one ES results in a decrease in several other ESs [8,9]. The reasons behind the trade-off or synergistic relationships among ESs are twofold: some types of ESs are directly linked, while others share common drivers [10]. Therefore, constructing sustainable landscapes requires accurately identifying the trade-offs and synergies among ESs to take effective measures to mitigate their negative tradeoffs and enhance their synergistic effects.
Currently, there are various approaches for identifying relationships among ESs. Among them, the most commonly used approach is spatial overlay analysis of different ES maps from a single time point [11,12,13,14,15]. This approach often calculates the spatial correlation of different ES types and judges whether there is a trade-off or synergy relationship between ESs based on the positive or negative correlation coefficient [16,17], or determines ESs that consistently co-occur in space through spatial clustering as synergy relationships [11]. The basic principle of these approaches is Space-for-time (SFT), initially applied in the field of ecology to address the lack of long-term observational data [18]. The assumption underlying the use of SFT approach to study ES trade-offs and synergies is the comparability of variability in ESs over time and space. This requires the initial conditions of all ESs to be homogeneous across the entire study area, and the driving factors causing changes in ESs to be consistent across the entire study area. However, research has shown that it is difficult to simultaneously meet these two conditions in large-scale ES studies [13], leading to potential misidentification of ES relationships.
Recently, many studies have highlighted the importance of considering temporal changes in identifying synergistic and trade-off relationships among ESs [13,19,20,21]. One such approach is the landscape Background Adjusted Space-For-Time method (referred to as BA-SFT). This approach involves spatially substituting time by analyzing the difference between the current ES values and historical landscape ES values. Its underlying assumption is that landscape history significantly influences the relationships among ESs [22]. For example, Shifaw et al. [23] estimated the changes in multiple ESs in the Upper Blue Nile Basin from 2000 to 2020. Results indicated that water yield, carbon sequestration, and soil retention primarily exhibited synergistic relationships, while habitat quality showed either no significant association with other ESs or trade-offs in some cases; Hu, et al. [24] assessed the variations in multiple ESs in Shanxi Province, China, from 2000 to 2020 and employed multiscale geographically weighted regression to identify the key drivers of ES changes; Wang et al. [25] mapped the spatial distribution of ES variations in the Sanjiangyuan region over the same period, revealing that most areas experienced simultaneous increases in provisioning and regulating ESs. Studies have shown that the BA-SFT approach, which considers landscape historical context, can mitigate some of the limitations of traditional SFT methods to some extent [13].
Additionally, with the development of remote sensing technology and Google Earth Engine, it has become possible to observe changes in ESs over long time series. Consequently, there has been a series of recent studies on the long-term temporal trends (TT) of ESs and their relationships. For instance, Liu, et al. [26] evaluated the impact of ecological policies on the long-term trends of ES using Inner Mongolia as a case study. Zhong, et al. [27] identified three linear and non-linear trends of ESs in the Yangtze River Delta (YRD) as an example. Qiu, Carpenter, Booth, Motew, Zipper, Kucharik, Loheide Ii, and Turner [21] explored changes in ES relationships at different scales based on future scenario data of ES. Temporal trend-based approaches often identify trade-offs and synergies among ESs by comparing the trends of various ESs [19]. If multiple ESs show the same increasing or decreasing trend over time, it indicates a synergistic relationship. Conversely, if one ES increases over time while another ES decreases, it indicates a trade-off relationship.
Currently, there are many case studies focusing on each of these approaches individually. However, it is not clear how the relationships between ESs identified by these three approaches differ, what are the advantages and disadvantages of each approach, and in what scenarios each approach should be chosen. Various methods have been applied within the SFT, BA-SFT, and TT frameworks to reveal complex relationships between ESs. These include traditional approaches like correlation analysis [28,29] and newer techniques such as Bayesian Belief Networks [30], Structural Equation Modeling [31], and dynamic models [32]. However, compared to correlation analysis, these new methods are less widely applicable within the SFT, BA-SFT, and TT frameworks. They also focus more on modeling causal relationships and dynamic changes between ESs, rather than directly identifying the type of interaction (synergy or trade-off) between specific ESs.
To compare the advantages and disadvantages of SFT, BA-SFT, and TT, we adopt correlation analysis as a unified method. Using the rapidly urbanizing YRD as a case study, we identify the synergy or trade-off relationships between 12 ESs based on annual data from 2001 to 2020. The study aims to address three questions: (1) Are the relationships among ESs identified by the three approaches the same? (2) Do the three approaches have different characteristics in identifying the relationships among ESs? (3) How to determine which approach should be adopted to quantify the relationships among ESs under what conditions?

2. Materials and Methods

2.1. Study Area

The YRD region is located in the eastern coastal region of China (Figure 1), encompassing cities such as Shanghai, Suzhou, and Nanjing, with a total area of approximately 211,700 km2 [27]. The climate characteristics of the YRD are mainly subtropical monsoon. The summers are hot and humid, with average temperatures ranging from 26 °C to 30 °C, and abundant rainfall, concentrated from June to August, accounting for 60% to 70% of the annual precipitation. Winters are relatively cold, with average temperatures ranging from 2 °C to 8 °C, and precipitation is relatively low, but often overcast and rainy. The terrain is predominantly flat, intersected by rivers such as the Yangtze and Huangpu Rivers. Soils consist mainly of alluvial and saline-alkali soils, conducive to crop cultivation. Major land use types in YRD include forests, croplands, urban land, and water bodies. The YRD is one of the most densely populated regions in China. Over the past few decades, this area has undergone rapid urbanization. While the rapid urbanization has greatly promoted the region’s economic development, it has also triggered a series of negative impacts, such as urban heat island, air pollution, and water scarcity. Recently, addressing urban ecological and environmental problems through nature-based solutions has become a key focus of high-quality development in this region [33].

2.2. Data Acquisition and Pre-Processing

We followed the classification of the United Nations Millennium Ecosystem Assessment [34] and selected ecosystem services (ES) that encompass all four ES categories, considering their relevance to the YRD region and data availability [35,36]. The selected ESs include one supporting service (habitat quality), two provisioning services (crop production and water yield), nine regulating services (urban cooling, flood risk mitigation, soil conservation, water retention, air purification, nitrogen retention, phosphorus retention, and carbon sequestration), and one cultural service (outdoor recreation). All the above ES data was primarily sourced from publicly available datasets previously published by our team [27]. Detailed methods for calculating each ES type are provided in the Supplementary Materials, and are summarized in Table 1. Due to the fact that county-level administrative units serve as both crucial administrative entities for ES management and the smallest units for available statistical data, this study selected county-level administrative units (n = 164) as the basic analysis units. Prior to calculating ES relationships, we aggregate all ES data at the raster scale into county-level administrative units and normalize them.

2.3. Three Approaches for Calculating Ecosystem Service Trade-Offs and Synergies

2.3.1. Space-for-Time Approach

For the space-for-time (SFT) approach, we utilize the Spearman correlation analysis to identify the relationships among ESs [20]. Specifically, we select 164 county-level administrative units in the YRD as sample points for a particular year and compute the Spearman correlation coefficient between each pair of the 12 types of ESs. A significant negative correlation between two types of ESs indicates a trade-off relationship, while a significant positive correlation suggests a synergistic relationship. Conversely, if there is no significant relationship between two types of ESs, we consider them to have no apparent relationship. Moreover, to evaluate the stability of the calculated relationships among ESs over time, we employ the SFT approach to calculate these relationships for each year spanning from 2001 to 2020. Subsequently, we utilize hierarchical clustering to group the relationships among ESs over time.

2.3.2. Landscape Baseline-Adjusted Space-for-Time Approach

Tomscha and Gergel [13] found that ignoring landscape history in the SFT approach can lead to misunderstandings of relationships among ESs. Therefore, the second approach employed in this study incorporates landscape history and is termed the baseline-adjusted space-for-time (BA-SFT) approach. Specifically, we first select two sets of ES maps for different time periods: one representing the current state of ES and the other representing the landscape baseline ES. Next, we subtract the landscape baseline ES map from the current ES map to obtain the spatial distribution map of ES changes (ΔES). Finally, we calculate the Spearman correlation coefficient and its significance level between changes in pairs of ESs and determine relationships between the two ESs based on this analysis. Additionally, to test the sensitivity of the identified relationships between ESs to different landscape history intervals, we compared the results of ES relationships for landscape history intervals of 5, 10, 15, and 20 years.

2.3.3. Temporal Trend Approach

Studies have shown that ESs and their relationships exhibit complex temporal dynamics [21,26]. Considering only a few snapshots in time can easily overlook the nonlinear changes and time lag effects of ES, leading to incorrect results. Therefore, the third approach used in this study is the temporal trend (TT) approach. Specifically, we first aggregate ESs at the county level administrative units in the study area to the entire study area scale. Then, we calculate the Spearman correlation coefficient between pairs of ES time series data spanning 20 years. Based on the correlation coefficient and its significance level, we determine the relationships between ESs.
Additionally, to further explore the spatial heterogeneity of ES relationships, we quantified the spatiotemporal patterns of each type of ES trend based on Sen’s slope and MK test methods [26]. If two ESs both show significant increasing or decreasing relationships, we consider there to be a synergy relationship between them. If one ES shows a significant increasing trend while the other shows a significant decreasing trend, we consider there to be a trade-off relationship between them.
Finally, we utilize the TLCC method to identify whether there is a time lag effect between different ES relationships, as well as its direction and strength [41,42]. The TLCC method determines the optimal time span required to achieve the best correlation by adjusting the time relative offset between different ESs, which can help determine the time lag patterns between two ESs. We assume that the relationship between the time series of ESs is correlated with any duration of time lag, represented by Equation (1):
r k x , y = c k x , y / δ x δ y + k ,
where   r k is the correlation coefficient at time lag point k , c k is the covariance between time series x t and time series y t + k , δ x is the standard deviation of time series x t , and δ y + k is the standard deviation of time series y t + k . The calculation of these statistical parameters is given by Equation (2):
c k x , y = ∑ t = 1 n − k x t − x t ¯ y t + k − y t + k ¯ / n − k δ x = ∑ t = 1 n − k x t − x t ¯ 2 / n − k δ y + k = ∑ t = 1 n − k y t + k − y t + k ¯ 2 / n − k ,
where the time lag k equals 0, ±1, ±2…Based on empirical evidence, the absolute value of the time lag k should be less than or equal to n/4 [41,42], which is 5 years in this study; y t + k ¯ is the mean of the lagged series y t + k , and x t ¯ is the mean of the time series x t .

2.4. Three Possible Incorrect Ecosystem Service Relationships

We compared the similarity between the SFT, BA-SFT, and TT methods by calculating the proportion of ES relationships identified by both methods in each ES. This proportion was determined by dividing the number of common relationships identified by the two methods by the total number of ES relationships related to that specific ES. For example, for air purification services, the similarity between SFT and BA-SFT is calculated as the percentage of collaborative/trade-off relationships identified by both methods out of the 11 relationships associated with air purification.
Based on Tomscha and Gergel [13], three potential errors in identifying relationships between ESs using different approaches are illustrated in Figure 2: (1) If the true relationship between two ESs is a trade-off, but the relationship identified by statistical methods is a synergy or no relationship, we refer to this as a trade-off missed. (2) If the true relationship between two ESs is a synergy, but the relationship identified by statistical methods is a trade-off or no relationship, we refer to this as synergy missed. (3) If there is no true relationship between two ESs, but statistical methods identify a trade-off or synergy relationship, we refer to this as an “interaction detected where none occur”.
We referenced a large number of existing studies to determine whether the ES relationships were correctly identified and based our judgment on two criteria: (1) ES trade-offs or synergies that are widely confirmed in the literature. For example, it is commonly found in the literature that there is a trade-off between food production and water retention, as agricultural land generally has lower water retention capacity compared to forest and grassland [14]; (2) the specific environmental and policy context of the YRD region. For instance, agricultural land expansion in the YRD occurred mainly before 2000, while the ES time scale we calculated is from 2001 to 2020, which could lead to misinterpretation of the relationship between crop production and soil conservation.

3. Results

3.1. Space-for-Time Approaches in Different Time Frames

Figure 3 illustrates the relationships between different types of ESs identified by the space-for-time (SFT) approach in 2020. Regarding provisioning services, positive significant correlations are observed between crop production and air purification, carbon sequestration, and water purification, while negative significant correlations are observed with outdoor recreation, soil conservation, urban cooling, and water yield. Water yield shows significant negative correlations with most ESs (except outdoor recreation, soil conservation, and water purification). As for regulating services, except for water purification, most types of regulating services show significant positive correlations with each other, while regulating services exhibit significant negative correlations or no significant relationship with provisioning services. Water purification service is negatively correlated with most regulating services, supporting services, and cultural services but positively correlated with provisioning services. Regarding cultural services, outdoor recreation is positively correlated with most regulating services but negatively correlated with crop production and water purification.
The hierarchical clustering results indicate that over the 20-year period from 2001 to 2020, most ES relationships remained stable, with a few ES relationships transitioning between trade-offs, synergies, and no significant relationships (Figure 4). The hierarchical clustering analysis categorizes changes of ES relationships into five groups. Category 1: No stable trade-offs or synergies, consisting mainly of ES relationships closely related to rainfall variability (water yield, soil conservation, water retention, flood risk mitigation) and air purification. Category 2: Strong trade-off relationships, primarily involving the relationships between water purification and other regulating, supporting, and cultural services. Category 3: Weak trade-off relationships, dominated by trade-offs between provisioning services and other regulating, cultural, and supporting services. Category 4: Weak synergy relationships, encompassing types of ESs where synergies between provisioning, regulating, supporting, and cultural services are relatively weak. Category 5: Strong synergy relationships, mainly involving various types of regulating services synergizing with each other.

3.2. Landscape Baseline-Adjusted Space-for-Time Approach

The comparison between 2020 and 2001 reveals both enhancements and declines in the distribution of the 12 types of ESs (Figure 5). Most ESs in 2020 show a decrease in areas with significant urbanization, particularly in the vicinity of Shanghai, compared to 2001. However, there are increases in crop production and air purification around Shanghai. Apart from a few ESs significantly influenced by rainfall variability (such as water yield, crop production, and water retention), most types of ESs exhibit increased capacity in the peripheral regions of the YRD, where urbanization is not as intense.
When employing the BA-SFT method, we detected significant trade-off or synergy relationships in only 44 out of 66 pairs of ES relationships (Figure 6). This contrasts significantly with the results obtained using the single-time-point SFT method. Specifically, there are synergistic relationships between crop provisioning and water retention, as well as water yield. Crop production exhibits no significant correlation with outdoor recreation or soil conservation. Water yield has no significant relationship with air purification, flood risk mitigation, habitat quality, or urban cooling, but it shows a significant positive correlation with carbon sequestration, water retention, and crop production. For regulating services, there is generally a positive correlation between regulating services and provisioning services, while water purification shows synergy relationships with most regulating services. In terms of cultural services, they correlated positively with most regulating services, including water purification, and show no significant relationship with crop production.
The relationships among ESs identified using the BA-SFT method are sensitive to the temporal intervals distancing from the landscape baseline (Figure 7). For instance, when the temporal interval is set to 5 years, flood risk mitigation exhibits a trade-off relationship with carbon sequestration, while crop production shows trade-off relationships with soil conservation and urban cooling, and air purification shows a trade-off relationship with urban cooling. However, when the temporal interval is changed to 10 years, 15 years, and 20 years, the trade-off relationship between flood risk mitigation and carbon sequestration may disappear or transform into a synergistic relationship, and the trade-off relationship between crop production and soil conservation may appear and disappear intermittently. The trade-off relationship between air purification and urban cooling disappears with longer time intervals, but a trade-off relationship between air purification and soil conservation emerges when the temporal interval is set to 20 years.

3.3. Temporal Trend Approach

At the regional scale, all 12 types of ESs exhibit three types of temporal trends (Figure 8): (1) Monotonic decreasing trend, such as habitat quality; (2) Fluctuating trend, mainly including several ES types closely related to precipitation fluctuations, such as water yield, flood risk mitigation, soil conservation, and water retention. (3) Unimodal trend. For example, water purification, crop production, and carbon sequestration mainly exhibit a trend of initially decreasing and then increasing, while air purification shows a trend of initially increasing and then decreasing.
The trends of all ES types exhibit significant spatial heterogeneity across different regions of the Yangtze River Delta (Figure 9). Specifically, the significant declining trends in habitat quality, urban cooling, flood risk mitigation, and outdoor recreation are primarily concentrated in highly urbanized areas such as Shanghai. For water yield, soil conservation, and water retention, they exhibit an increasing trend in most parts of the YRD, with significant growth trends mainly concentrated in the southern regions, and only a small portion of the northern areas showing a significant decrease. As for crop production, most areas of the YRD demonstrated a significant increase, with only some central and southern regions showing a significant decrease. Regarding carbon sequestration, the majority of the YRD region exhibits a significant increase, with decreasing trends sparsely distributed along the coastal areas of the YRD.
At the regional scale, the Temporal Trend (TT) approach detected a very limited number of relationships among ESs, but they all exhibited synergy in direction (Figure 10). Specifically, the TT Approach identified a total of 10 significant relationships among ES, accounting for 15% of all ES relationships. For provisioning services, crop production showed a significant positive correlation with carbon sequestration and soil conservation. Water yield was significantly positively correlated with habitat quality, outdoor recreation, and soil conservation. For regulating services, water retention was positively correlated with soil conservation and habitat quality, while urban cooling was positively correlated with habitat quality, and Nitrogen retention was positively correlated with Phosphorus retention. For cultural services, outdoor recreation was only significantly positively correlated with water yield.
The Temporal Trend (TT) approach can identify spatial heterogeneity in the trade-offs and synergies among ESs. This study demonstrates the spatial distribution of trade-offs and synergies between crop production and other service types as an example (Figure 11). At the county level, there are regions where crop production exhibits both trade-offs and synergies with almost all other ES types. Trade-offs relationships, predominantly between crop production and habitat quality, air purification, urban cooling, and outdoor recreation, are mainly located in the central part of the YRD. Conversely, synergistic relationships, predominantly between crop production and carbon sequestration, are mainly distributed in the northern part of the YRD. Additionally, there are significant areas where crop production shows no significant relationship with any other ES type.

4. Discussion

4.1. Little Disagreement Between Three Approaches

We deeply explored the advantages, disadvantages, and applicability of each method by comparing the overlap of ES relationships identified by the three approaches (SFT, BA-SFT, and TT). The results of this study demonstrate great differences in the relationships identified among ES by the three approaches (Table 2). The proportion of identical ES relationships identified by all three approaches was only 1.45%. The consistency in ES relationships detected by the BA-SFT method compared to the TT and SFT methods is 46.97% and 42.42%, respectively, while the consistency between the TT and SFT methods is only 15.15%. In the comparison between the BA-SFT and TT methods, ES relationships associated with air purification exhibit relatively high consistency, reaching 75%. However, in the comparison between the SFT and TT methods, ES relationships related to outdoor recreation and water production both show extremely low consistency, at only 8.33%.
By comparing the similarities in the relationships between ESs across three methods—SFT, BA-SFT, and TT—our results show that compared to the SFT method, the BA-SFT method identifies a greatly lower number of synergies and trade-offs between ESs (Figure 12). (1) For provisioning services, in the SFT method, crop production is unrelated to water retention and is in a trade-off relationship with water yield, whereas in the BA-SFT method, both are in a synergistic relationship. In the SFT method, crop production is negatively correlated with outdoor recreation and soil conservation, while in the BA-SFT method, there is no significant correlation. Water yield is negatively correlated with air purification, carbon sequestration, flood mitigation, habitat quality, water retention, and urban cooling in the SFT method, but shows no significant relationship with air purification, flood risk mitigation, habitat quality, and urban cooling in the BA-SFT method. However, it is significantly positively correlated with carbon sequestration, water retention, and crop production. (2) For regulating services, in the SFT method, most regulating services were in trade-off relationships with habitat quality and provisioning services, while in the BA-SFT method, regulating services are mostly positively correlated with provisioning services. In the SFT method, water purification is in trade-off relationships with most regulating services, while in the BA-SFT method, they are mostly in synergy relationships. (3) For cultural services, in the SFT method, they are positively correlated with most regulating services but negatively correlated with crop production and water purification. In the BA-SFT method, they are positively correlated with most regulating services, including water purification, and not significantly correlated with crop production.
Compared to the other two methods, the TT method identifies fewer significant trade-offs and synergies between ESs. On one hand, many ES relationships identified by the other two methods are not detected by the TT method. For example, both the SFT and BA-SFT methods show a trade-off relationship between crop production and urban cooling, but the TT method identifies no significant relationship between them. On the other hand, the TT method identifies trade-offs and synergies between ESs that are not detected by the other two methods. For instance, while both the SFT and BA-SFT methods show no relationship between water yield and outdoor recreation, the TT method reveals a significant positive correlation between them. Lastly, the TT method can identify relationships that are completely opposite to those identified by the other two methods. For example, while the SFT method shows a trade-off relationship between crop production and soil conservation, the TT method identifies a synergy relationship between them.

4.2. Mechanisms by Which the Three Approaches Produce Different ES Relationships

4.2.1. Ignoring the Assumptions of the SFT Method May Lead to the Identification of Incorrect ES Relationships

The advantage of using the SFT method is that spatial variability and temporal variability are comparable [18]. Therefore, this method is only effective when landscape history is not important for ES and when the driving forces of ES remain consistent across the entire region. However, in regions like the YRD where there is strong spatial heterogeneity in topography, climate, and hydrology, this assumption is difficult to meet. For example, many studies have shown that crop production and water retention often show a trade-off relationship because agricultural land has a lower water retention capacity compared to other land use types such as forests and grasslands [14,43,44]. However, in this study, the SFT method did not detect this trade-off relationship in the YRD region. This could be because the factors affecting water retention include not only vegetation types but also rainfall and evapotranspiration, which vary significantly across the entire YRD and exhibit substantial spatial heterogeneity [27,45]. Therefore, the SFT method is likely to fail to identify the trade-off relationship between crop production and water retention.

4.2.2. The Lack of Sufficiently Early Landscape Background ES Data Leads to the Misidentification of ES Relationships by the BA-SFT Method

Our results indicate that the relationships between ESs identified based on the BA-SFT method are significantly influenced by the temporal interval from the historical landscape baseline (Figure 7). Compared to the SFT method, the BA-SFT method identifies fewer relationships between ESs, potentially leading to a higher number of missed trade-offs and synergies. For instance, under normal circumstances, there should be a trade-off relationship between crop production and soil conservation [14,46]. However, our BA-SFT results did not identify this trade-off relationship (Figure 6). The reason for missed trade-offs may be attributed to the short temporal interval of the distance landscape background values chosen in our study (only 20 years), whereas extensive expansion of agricultural land in YRD occurred before 2000, predating the earliest available landscape baseline data. Furthermore, the BA-SFT method may encounter identification errors when identifying relationships between ES types with strong fluctuations and other ESs. For example, flood risk mitigation and carbon sequestration often exhibit a synergistic relationship. However, our research findings indicate that only with longer temporal intervals from the landscape background (15 and 20 years), can the synergistic relationship be correctly identified, while at a 5-year temporal interval, it is identified as a trade-off relationship, and at a 10-year interval, it is identified as having no relationship (Figure 7).

4.2.3. The Time Span of Time Series Data, Data Aggregation Effects, and Time Lag Effects Lead to Errors in Identifying Relationships Between ES in the TT Method

Our results indicate that at the regional scale of the entire YRD area, the relationships between ES identified by the TT method are significantly lower compared to SFT and BA, and three types of identification errors, namely trade-off missed, synergy missed, and Interactions detected where none occur, are all present (Figure 12). For example, crop production and urban cooling are generally assumed to be in a trade-off relationship, but the TT method used in this study failed to identify this trade-off relationship. Crop production and soil conservation should ideally exhibit a trade-off relationship, but the TT method in this study identified a synergy relationship. Water retention and flood risk mitigation are supposed to be in synergy but were identified as having no relationship in this study. Water yield and outdoor recreation were expected to have no relationship, but the TT method in this study identified a synergy relationship.
The reasons for the TT method’s misidentification of trade-offs and synergies between ES may include three aspects: (1) Insufficient length of time series data for ES in this study may fail to capture long-term trade-offs and synergies resulting from changes in ES. For instance, trade-off relationships resulting from conversions between cropland and forests or grasslands may have occurred in earlier periods of the landscape. (2) Data aggregation effects may obscure ES relationships caused by local landscape changes. Previous studies have shown that data aggregation can influence the results of trade-offs and synergies in ES [13,21]. In this study, the TT method aggregated ES data to the regional scale, potentially masking relationships among ES due to local landscape changes. To verify this point, we analyzed the relationships between crop production and other ES at the grid scale. The results showed that trade-off and synergy areas exist between crop production and all other ES types across the landscape, indicating that aggregating data for the entire study area may obscure local variations. (3) Time lag effects may lead to the misidentification of relationships between some ES. Studies have suggested the existence of time lag effects between trade-offs and synergies among ecosystem services [28,47]. Our time lag analysis results also indicate that time lag effects exist between some ESs, especially those closely related to climate factors (such as water yield, urban cooling, and water retention), showing higher time lagged synergy relationships (Please see the Supplementary Materials). Time lag effects may lead to the misidentification of trade-offs and synergies between ES by the TT method.

4.3. Implications for Choosing Suitable Methods for Calculating ES Relationships

Correctly identifying trade-offs and synergies between ESs is of crucial importance for sustainable ecosystem management [8]. When trade-off relationships between ESs are mistakenly identified as synergies, management policies aimed at enhancing one ES capacity may lead to unexpected losses in other ESs. Conversely, if synergies between ESs are misidentified as trade-offs, opportunities to simultaneously enhance the functionality of both ESs may be missed [13,19]. The findings of this study provide several insights for selecting appropriate methods to identify trade-offs and synergies between ESs:
The advantage of using the SFT method lies in its minimal data requirements, as it can identify relationships between ESs without the need for time series data. However, its use is constrained by the assumption that the original conditions of ESs in the region are relatively consistent [18]. Our results indicate that in large regions such as the YRD, there are significant climatic differences, resulting in considerable variations in the original conditions of ESs related to climate. Consequently, this leads to the misidentification of trade-offs and synergies between related ESs. Therefore, we recommend carefully analyzing the initial conditions of each ES type to determine their homogeneity and whether the main drivers of ES changes are consistent across the entire study area before using the SFT method to identify relationships between ESs.
The BA-SFT method requires a higher level of data, necessitating spatial distribution data of ESs at different time points. Its advantage lies in its ability to overcome the limitations of the SFT method caused by background heterogeneity [13,19]. However, our results indicate that the BA-SFT method is sensitive to the selection of the landscape background’s time intervals. Insufficient time intervals may result in the inability to identify many synergies and trade-offs between ESs. Additionally, our findings also suggest that the BA-SFT method may produce errors in identifying relationships between nonlinearly changing ESs, such as those influenced by strong climate fluctuations like water retention and water yield.
The TT method has the highest data requirements, necessitating long-term time series data of ESs on an annual basis. Compared to the previous two methods, its advantages include: (1) the ability to explore relationships between ESs at the entire regional scale and spatially display the spatial heterogeneity of these relationships, (2) the capability to identify nonlinear relationships between ESs, (3) the ability to identify temporal lag effects between ESs. However, similar to the BA-SFT method, the TT method also requires long time series data to effectively identify the trade-offs and synergies between ESs caused by drivers with larger temporal scales of change.
Overall, our results indicate that the TT method is the preferred approach for identifying relationships between ESs given a sufficiently long time series of data. This method excels in recognizing both linear and nonlinear trade-offs and synergies between ESs, as well as detecting temporal lag effects between them. However, it is important to note that time series data may not be available in all regions. In areas where time series data are limited, the BA-SFT method performs better. Compared to the SFT method, BA-SFT can better account for the influence of landscape history. However, its accuracy is significantly influenced by the interval of distance landscape baseline data, and it is not suitable for analyzing relationships between ES with nonlinear changes. Additionally, both the TT method and the BA-SFT method require long historical data, or else there is a risk of misidentifying ES relationships. If historical ES data are unavailable, the SFT method is the only option. However, before using this method, it is essential to ensure that the initial conditions of ES in the study area are similar and that the drivers of ES change are consistent across the entire study area.
Moreover, this study has certain limitations. We employed correlation analysis as a unified method to compare the advantages and disadvantages of SFT, BA-SFT, and TT. However, correlation analysis can only identify linear relationships between ESs. Future research should overcome this limitation by exploring more advanced methods applicable within the SFT, BA-SFT, and TT frameworks. For instance, Bayesian Belief Networks [30], Structural Equation Modeling [31], dynamic models [32], and multivariate regression trees [48] can help uncover the complex nonlinear and causal relationships between ESs. Additionally, the spatial patterns of trade-offs and synergies among ESs should also be incorporated. Methods such as geographically weighted regression [49] and bivariate spatial autocorrelation analysis [50] can be used to identify the spatial heterogeneity of ES relationships.

5. Conclusions

This study systematically compared three approaches, namely space-for-time approach, landscape background-adjusted space-for-time approach, and temporal trend approach, for identifying relationships between ESs in the Yangtze River Delta region. We found great differences in the relationships detected among ESs by these three approaches, with only 1.45% consistency observed among 66 pairs of ES relationships. All three approaches exhibited errors such as trade-offs missed, synergies missed, and interactions detected where none occur. The reasons for misidentifying ES relationships by these approaches include ignoring the assumptions of the research methods, short time intervals in distance landscape background, insufficient time series data of ES, data aggregation effects, nonlinear changes in ESs, and time lag effects. Our study results indicate that each of the three approaches has its strengths and limitations in identifying trade-offs and synergies between ES. Before selecting a method to identify relationships between ES in a particular study area, it is important to consider the availability of time series data, the characteristics of ES changes, and carefully assess the assumptions and uncertainties of each method.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14030644/s1. Refs. [38,40,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70] are cited in the Supplementary Materials file.

Author Contributions

Conceptualization, X.F.; Methodology, Z.Z.; Software, B.Z.; Formal analysis, Z.Z. and B.Z.; Writing—original draft, Z.Z., B.Z. and X.F.; Writing—review & editing, L.K. and X.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (No. 42201101).

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. The location and land use/cover change of the study area.
Figure 1. The location and land use/cover change of the study area.
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Figure 2. Different approaches with limitations can identify three incorrect ecosystem service relationships: trade-offs missed, synergies missed, interactions detected where none occur. “T”: Trade-off, “S”: Synergy, “N”: No interaction. This figure is adapted from Tomscha and Gergel [13].
Figure 2. Different approaches with limitations can identify three incorrect ecosystem service relationships: trade-offs missed, synergies missed, interactions detected where none occur. “T”: Trade-off, “S”: Synergy, “N”: No interaction. This figure is adapted from Tomscha and Gergel [13].
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Figure 3. Matrix of Spearman correlations between different ecosystem services based on the space-for-time approach. *: Significant at the 0.05 level. **: Significant at the 0.01 level.
Figure 3. Matrix of Spearman correlations between different ecosystem services based on the space-for-time approach. *: Significant at the 0.05 level. **: Significant at the 0.01 level.
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Figure 4. Temporal stability of relationships between ecosystem services based on the space-for-time approach.
Figure 4. Temporal stability of relationships between ecosystem services based on the space-for-time approach.
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Figure 5. The spatial distribution of differences in ecosystem services from 2001 to 2020 (∆ES).
Figure 5. The spatial distribution of differences in ecosystem services from 2001 to 2020 (∆ES).
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Figure 6. Matrix of spearman correlations between different ecosystem services based on the landscape baseline-adjusted space-for-time approach. *: Significant at the 0.05 level. **: Significant at the 0.01 level.
Figure 6. Matrix of spearman correlations between different ecosystem services based on the landscape baseline-adjusted space-for-time approach. *: Significant at the 0.05 level. **: Significant at the 0.01 level.
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Figure 7. Sensitivity to time intervals of relationships between ecosystem services identified based on the landscape baseline-adjusted approach.
Figure 7. Sensitivity to time intervals of relationships between ecosystem services identified based on the landscape baseline-adjusted approach.
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Figure 8. Trends in different ecosystem service types over time at the regional scale.
Figure 8. Trends in different ecosystem service types over time at the regional scale.
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Figure 9. Spatial distribution of trends in different ecosystem service types.
Figure 9. Spatial distribution of trends in different ecosystem service types.
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Figure 10. Matrix of Spearman correlations between different ecosystem services based on the temporal trend approach. *: Significant at the 0.05 level. **: Significant at the 0.01 level.
Figure 10. Matrix of Spearman correlations between different ecosystem services based on the temporal trend approach. *: Significant at the 0.05 level. **: Significant at the 0.01 level.
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Figure 11. Spatial distribution of relationships between crop production and other types of ecosystem services.
Figure 11. Spatial distribution of relationships between crop production and other types of ecosystem services.
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Figure 12. Comparison of the relationships between ecosystem services identified by three approaches.
Figure 12. Comparison of the relationships between ecosystem services identified by three approaches.
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Table 1. The selected ecosystem services, along with their descriptions and calculation methods.
Table 1. The selected ecosystem services, along with their descriptions and calculation methods.
Ecosystem Service TypesDescriptionQuantification Methods
  • Habitat quality
The ability of ecosystems to provide organisms with resources for survival and reproductionInVEST
2.
Crop production
Capacity of farmland to produce crop[37]
3.
Water yield
Capacity of ecosystems to provide freshwaterInVEST
4.
Urban cooling
Capacity of ecosystems to reduce the urban heat island effectUrban InVEST
5.
Flood risk mitigation
Capacity of urban ecosystems to reduce urban flood riskUrban InVEST
6.
Soil conservation
Reduction in soil erosion due to the presence of vegetationInVEST
7.
Water retention
Available water retained by ecosystemsInVEST
8.
Air purification
Amount of airborne pollutants absorbed by ecosystems.[38]
9.
Nitrogen retention
Amount of nitrogen pollutants prevented from entering runoff by ecosystemsInVEST
10.
Phosphorus retention
Amount of phosphorus pollutants prevented from entering runoff by ecosystemsInVEST
11.
Carbon sequestration
Amount of carbon dioxide fixed by ecosystems[39]
12.
Outdoor recreation
Opportunities for outdoor activities provided by ecosystems[40]
Table 2. Percentage of matches between ecosystem service relationships identified by the three methods.
Table 2. Percentage of matches between ecosystem service relationships identified by the three methods.
Ecosystem Service TypesSFT & BA-SFT (%)SFT & TT (%)BA-SFT & TT (%)Three Approaches (%)
Air purification33.3325.0075.0016.67
Crop production66.6741.6741.6733.33
Carbon sequestration58.3316.6733.3316.67
Flood risk mitigation58.3316.6758.3316.67
Habitat quality66.6733.3341.6733.33
Nitrogen retention41.6716.6741.6716.67
Outdoor recreation66.678.3333.338.33
Phosphorus retention41.6725.0041.6725.00
Soil conservation33.3325.0050.0016.67
Urban cooling58.3316.6758.3316.67
Water retention58.3333.3341.6725.00
Water yield33.338.3350.008.33
All46.9715.1542.421.45
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Zhong, Z.; Zhou, B.; Kong, L.; Fang, X. When to Use What: A Comparison of Three Approaches to Quantify Relationships Among Ecosystem Services. Land 2025, 14, 644. https://doi.org/10.3390/land14030644

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Zhong Z, Zhou B, Kong L, Fang X. When to Use What: A Comparison of Three Approaches to Quantify Relationships Among Ecosystem Services. Land. 2025; 14(3):644. https://doi.org/10.3390/land14030644

Chicago/Turabian Style

Zhong, Zhen, Bochuan Zhou, Lingqiang Kong, and Xuening Fang. 2025. "When to Use What: A Comparison of Three Approaches to Quantify Relationships Among Ecosystem Services" Land 14, no. 3: 644. https://doi.org/10.3390/land14030644

APA Style

Zhong, Z., Zhou, B., Kong, L., & Fang, X. (2025). When to Use What: A Comparison of Three Approaches to Quantify Relationships Among Ecosystem Services. Land, 14(3), 644. https://doi.org/10.3390/land14030644

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