Ecology and SocietyEcology and Society
 E&S Home > Vol. 19, No. 1 > Art. 20
The following is the established format for referencing this article:
Felipe-Lucia, M. R., F. A. Comín, and E. M. Bennett. 2014. Interactions among ecosystem services across land uses in a floodplain agroecosystem. Ecology and Society 19(1): 20.
http://dx.doi.org/10.5751/ES-06249-190120
Research

Interactions Among Ecosystem Services Across Land Uses in a Floodplain Agroecosystem

1Instituto Pirenaico de Ecología-CSIC., 2Instituto Pirenaico de Ecología-CSIC, 3Department of Natural Resource Sciences and McGill School of Environment, McGill University

ABSTRACT

Managing human-dominated landscapes such as agroecosystems is one of the main challenges facing society today. Decisions about land-use management in agroecosystems involve spatial and temporal trade-offs. The key scales at which these trades-offs occur are poorly understood for most systems, and quantitative assessments of the services provided by agroecosystems under different combinations of land uses are rare. To fill these knowledge gaps, we measured 12 ecosystem services (ES), including climate regulation, gas regulation, soil stability, nutrient regulation, habitat quality, raw material production, food production, fishing, sports, recreation, education, and social relationships, in seven common land-use types at three spatial scales, i.e., patch, municipality, and landscape, in a riparian floodplain in Spain. We identified the provision of each ES in each land-use type either by direct measurement or from public databases. We analyzed the interactions, i.e., trade-offs and synergies, among ES across land uses and spatial scales and estimated ES provision in several land-use change scenarios. Our results illustrated that each land-use type provides unique bundles of ES and that the spatial scale at which measurements were taken affected the mixture of services. For instance, a land-use type with low provision of services per hectare but with an extensive area can supply more services to the overall landscape than a land-use type supplying higher values of services per hectare but with a smaller extent. Hence, riparian forest supplied the most service of any land-use type at the patch scale, but dry cereal croplands provided the most services across the municipality and landscape because of their large area. We found that most ES should be managed primarily at the patch scale, but food production, fishing, and social relationships were more relevant to manage at the municipality scale. There was great variability in ES interactions across scales with different causes of trade-offs at each scale. We identified more significant synergies among ES than trade-offs. Trade-offs were originated because some services were mutually incompatible within a given land use, whereas the provision of others depended on land-management decisions within a land-use type. Thus, we propose a classification of ES interactions that incorporates societal values as drivers of management decisions along with biophysical factors as likely causes of ES trade-offs and conclude with practical suggestions to reduce trade-offs and to enhance the supply of multiple ES to society.
Key words: agroecosystem; ecosystem services; floodplain; interactions; land uses; spatial scales; trade-offs

INTRODUCTION

Agroecosystems are managed to fulfill basic human needs, such as food and raw materials (Zhang et al. 2007). They occupy 40% of the global terrestrial surface (FAO 2009, as cited in Power 2010), of which 3.5% are on floodplains (Tockner and Standford 2002). Floodplains sustain a large portion of the world’s food production thanks to their nutrient-rich and water-abundant soils. Indeed, great parts of floodplains’ extent are dedicated to agricultural production, from 11% in African rivers to 79% in European rivers (Tockner and Standford 2002). Current pressure on floodplain agroecosystems to feed the growing human population is leading to major environmental degradation, including deforestation, soil erosion, nutrient leaching and water abstraction, diversion, and pollution (Simoncini 2009). This is especially important given that floodplains are one of the most endangered habitats and biodiversity hotspots while still the second highest worldwide attraction for housing developers (Moss and Monstadt 2008). Floodplains are key ecosystems for land managers because of their important role in food supply, human settlement, and biodiversity conservation.

Decisions about land-use planning, in floodplains and elsewhere, generally involve spatial and temporal trade-offs for society and ecosystems (Box 1). Consequences of these trade-offs need to be assessed across temporal and spatial scales by policy makers prior to management actions such that managers can make effective decisions (Rodríguez et al. 2005, 2006, Tallis and Polasky 2009, Cabell and Oelofse 2012). Such assessments are paramount to maximizing human well-being, enabling adaptive management, and improving resilience in the social-ecological system (Carpenter et al. 2005). The spatial patterns of social-ecological systems, e.g., the number, location, and relative proportion of different land-use types, can vary at differing spatial scales, which can then influence ecological functions (Pringle et al. 2010). Repercussions of outcomes at a particular spatial scale may affect biodiversity and ecosystem conservation, as well as stakeholder interests and institutional responsibilities (Hein et al. 2006). Thus, to make effective land-management decisions, baseline data about the biophysical and social settings are required at the spatial scales of the decisions being made (DeFries et al. 2004, Nicholson et al. 2009). Effects of management actions may have different results across spatial scales (Concepción et al. 2012), e.g., at the individual patch level compared to a municipality, or entire landscape. Therefore high quality local data and multiscale analyses are fundamental to design adequate management plans, understand the trade-offs they encompass, and facilitate decision making (Carpenter et al. 2009).

To orient decision makers to identifying trade-offs and synergies in land-use planning, many studies have applied the concept of ecosystem services (ES; Costanza et al. 1997, de Groot et al. 2002), i.e., the benefits humans obtain from ecosystems, such as clean air, water, food, raw materials, and recreation (Rose and Chapman 2003, Bennett and Balvanera 2007, Barral and Maceira 2012, Rathwell and Peterson 2012). The amount of each ES supplied in a given area depends on both the per hectare provision of service by land-use type and the total amount of each land use found in the study area. Moreover, ES do not operate independently from each other (Pereira et al. 2005), but they interact in trade-offs and synergies. There is also evidence that ES act differently across both spatial and temporal scales (Swift et al. 2004, Rodríguez et al. 2005, Power 2010), and that land-use patterns affect ES provision (Mitchell et al. 2013); however, the key scales for ES management still remain poorly understood (Hein et al. 2006). Most studies of multiple ES use GIS and satellite images (Kreuter et al. 2001, Konarska et al. 2002, Chen et al. 2009), global databases (Viglizzo and Frank 2006, Tianhong et al. 2010), or models to estimate ES provision (Nelson et al. 2009, Goldstein et al. 2012). Few studies however, have gathered local data across land uses (but see Raudsepp-Hearne et al. 2010), despite evidence that these data are critical to accurate assessment of service provision (Eigenbrod et al. 2010).

Floodplains contribute to the provision of more than 25% of all terrestrial ES (Tockner and Standford 2002). Therefore, understanding ES interactions in floodplain agroecosystems is an important challenge in ecology. Understanding how floodplains can be managed across spatial scales to deliver multiple ES could enhance the supply of ES to society, providing land managers with decision-making tools to reach win-win or small loss-big gain solutions (DeFries et al. 2004) for policy making.

We aim to identify: (1) how the supply of a set of ES changes across land-use types and spatial scales in a floodplain; (2) which trade-offs and synergies are common or different in each land-use type and across spatial scales; and (3) how land-use change might affect the provision of ES. We evaluated 12 ES in 7 land-use types identified within a river floodplain at multiple spatial scales, i.e., patch, municipality, and landscape, using ecological indicators. We illustrate significant differences in the supply of ES across land uses, spatial scales, and alternative scenarios; and we analyze their interactions, i.e., trade-offs and synergies. Finally, we discuss major questions on ES interactions and suggest practical land-use management applications.
Box 1. Definitions applied to ecosystem services.

Trade-off: Situation in which land use or management actions increase the provision of one ecosystem service and decrease the provision of another. This may be caused by simultaneous responses to the same driver or caused by true interactions among services (Bennett et al. 2009).

Synergy: Situation in which the combined effect of a number of drivers acting on ecosystem services is greater than the sum of their separate effects (adapted from Carpenter et al. 2005). In other words, a synergism occurs when ecosystem services interact with one another in a multiplicative or exponential fashion (Rodríguez et al. 2006). These can be positive, i.e., multiple services improving in provision, or negative, i.e., multiple services declining in provision.

METHODS

Study site

The study area is the floodplain of the Piedra River in central Spain (Fig. 1). The Piedra River is 76 km long and the watershed covers an area of 922.72 km². The river floodplain ranges from 50 to 300 m wide and occupies 19.3 km². It is composed of 12 municipalities in which 1539 people live permanently (Table 1), although the population can double during the summer (Felipe-Lucia 2012). The floodplain is commonly split into three parts, i.e., upper, central, and downstream, based on the amount of water available for agricultural use. In the upper floodplain, the river is dry for most of the year and dry cereal crops are cultivated. The central floodplain, which is rich in water springs, is devoted to highly productive irrigated cereal crops and poplar groves. The downstream floodplain, separated from the central floodplain by a reservoir, is characterized by orchards, fruit groves, and abandoned agricultural lands. There are also two main natural areas in the watershed. One of them is located in the upper floodplain gorges and the other, just upstream from the reservoir, is a private natural park whose waterfalls attract thousands of tourists each year.

Data gathering and analyses

We selected 12 ES to measure based on their importance for the ecological functioning of a river floodplain (see Harrison et al. 2010) and our ability to assess them in the study area (Table 2). We estimated the provision of these ES in seven different land-use types common to the Piedra River floodplain (Table 3). We measured the area of each land-use type using the latest Spanish crop and land-use digital map (Ministerio de Medio Ambiente Y Medio Rural y Marino 2009) with ArcGIS 10 (ESRI 2012) and validated these measurements with field observations. We assessed ES at three spatial scales, i.e., patch, municipality, and landscape, in which municipality scale comprises the portion of each municipality within the river floodplain, ranging from 0.5 to 3.4 km² in area and including several land-use types, and landscape scale refers to the entire floodplain of the Piedra River catchment.

To assess ES provision, we either estimated the ES indicators directly or obtained the values for ES indicators from public databases (Table 2, see Appendix 1 for details). For directly sampled ES, we surveyed three 0.5 ha patches in representative sites of each land-use type distributed throughout the river floodplain. Within each of these patches, three floodplain-wide transects perpendicular to the river channel were established 25 m apart. The appropriate indicators were measured at 1 m, 5 m, and 15 m away from the river along each transect. These values were averaged to determine the overall mean provision of services in that land-use type at the patch scale. Data obtained from databases were available as average values per hectare by land-use type, except for the services of fishing areas and sports, which were available as mapped trails and their length was measured using GIS tools.

For regulating, supporting, and provisioning services, we first quantified ES provided within individual patches of unique land use, and used this data to estimate total provision at the municipality and landscape scales based on the total area of each land-use type at each of these scales. Thus, to scale from the patch to municipality, ES values at the patch scale were multiplied by the extent of each land-use type within each municipality. Average values of each ES by land-use type across all municipalities were used at the municipality scale. To scale up to the landscape, ES values at the patch scale were multiplied by the total extent of each land-use type in the whole river floodplain.

Cultural services were measured at the municipality scale, rather than the patch, and therefore cultural ES were downscaled from the municipality scale to the patch scale by dividing the ES value per municipality by the extent of each land use within each municipality, and averaging. To scale up cultural ES to the landscape, ES values at the municipality scale were aggregated by land-use type (further information about spatial scale adaptation is provided in Appendix 1).

To determine the key spatial scale to manage land-use planning based on the provision of ES, we compared the amount of each service provided relative to other services across the patch, municipality, and landscape scales. To do this, we estimated the proportion of each land-use type at each spatial scale and multiplied it by the ES provision values of each land-use type at its corresponding scale (Table 3). We expected this scaling technique to be useful to discriminate the provision of ES by a range of land-use types at different spatial scales because land-use extension is independent from municipalities and the landscape. Finally, to simulate scenarios in which a single land use occupied the entire floodplain landscape, we multiplied the ES supply per hectare of each land-use type by the extent of the floodplain landscape.

We plotted these results using the graphics package (Murrell 2005) of the statistical software R (R Development Core Team 2012). To detect significant differences in the provision of ES among the studied land uses and spatial scales, we performed generalized linear mixed models (GLMM) fitted with the Poisson family distribution using the ‘lme4’ R package (Bates et al. 2012). For this, we estimated each ES (response variable) as a function of the interaction between land-use type and spatial scale (categorical variables). Models were validated by checking the model residual plots (Zuur et al. 2009). We performed multiple comparison tests (‘multcomp’ R package; Hothorn et al. 2008) and figures plots (‘effects’ R package; Fox 2003) to determine significant differences among the means. Finally, to test the significance of ES interactions and their directions, i.e., positive or negative, Spearman correlation from the ‘AED’ R package (Zuur et al. 2009) was applied. Interactions were considered significant positives, but not necessarily synergies, when r² > 0.5 and significant negatives, and thus, trade-offs, when r² < -0.5. We also considered the interactions among ES by each land-use type separately using the same techniques.

RESULTS

Changes in the supply of ecosystem services across land-use types and spatial scales

Each land-use type in our study provided unique mixtures and quantities of ES, but some land uses did not provide some ES, for example, urban areas did not supply nutrient regulation. We also noticed that the importance of each land-use type in supplying ES varied across the spatial scales studied (Table 4; see also Appendix 2, Fig. S1). For instance, at the patch scale, riparian forest supplied more soil stability, nutrient regulation, habitat quality, sports, recreation, and education than any other land-use type. Similarly, fruit groves supplied more climate and gas regulation and raw materials. However, at the municipality and landscape scales, the key service suppliers changed, primarily because of the amount of land in each land-use type. So, for example, dry cereal cropland supplied the most climate regulation, soil stability, nutrient regulation, habitat quality, food production, and social relationships across the whole landscape, whereas fruit groves were the main supplier of gas regulation. Riparian forest also supplied the most sports, education, and recreation at the municipality and landscape scales.

Across all three spatial scales, three land uses consistently supplied ES in larger amounts than other land uses. They were riparian forests, i.e., ES provided largely in riparian forests were sports, recreation, and education; fruit groves, i.e., gas regulation and raw materials; and dry cereal crops, i.e., social relationships. These land uses remain important across scales because they either supply an elevated level of service or cover a fairly extensive area. Further information about the supply of ES by each land use across spatial scales is depicted in Appendix 2 (Fig. S1).

In addition, the comparison among spatial scales about their relative provision of ES showed larger values for most ES at the patch scale, suggesting this is the key scale to manage ES in our study area. However, values of food production, fishing, and social relationships were larger at the municipality scale (Fig. 2; see also Appendix 2, Fig. S2, Tables S1-S3).

Simulated scenarios

Simulating scenarios in which a single land use occupied the entire floodplain landscape of the study area resulted in a large range of variation in ES provision. We observed that a landscape composed completely of the riparian forest would increase the widest variety of ES, namely, habitat quality, nutrient regulation, soil stability, and the majority of cultural services; a landscape specialized in fruit groves would have high levels of gas and climate regulation and raw materials production; a landscape covered by irrigated cereal crops would maximize food production; and a landscape dedicated to dry cereal crops would enhance social relationships (Fig. 3). Because of the differences in services provided across different land-use types, ultimately, preserving a mixture of land-use types is critical to providing a mixture of services in the landscape.

Ecosystem services interactions: trade-offs and synergies across spatial scales and land-use types

Relationships between ES varied across spatial scales. That is, some interactions, as measured by correlation, between ES were significant only at a single scale whereas others were significant at multiple spatial scales. Across scales, 21% of ES interactions varied in significance and 19% reversed from positive to negative or vice versa. The significant interactions between ES were 96% positive and just 4% negative (Table 5). However, only four of the significant positive interactions were consistent across all three spatial scales: the synergies among soil stability, nutrient regulation and habitat quality, and the synergy between recreation and education. Significant negative interactions were only observed between climate regulation and two other services, fishing and sports, at the patch scale. Finally, we found the largest number of significant positive interactions between ES at the scale of the municipality, especially between cultural and provisioning services.

Including the land-use type as a factor in ES interactions revealed that the only significant correlations were between cultural services, and they were all positive. Surprisingly, urban areas were the only land use in which all cultural services correlated among them. In riparian forests, fishing, recreation, education, and social relationships were also correlated. Finally, fishing and sports were correlated in all land uses except in riparian forest and abandoned crops (Table 6).

DISCUSSION

The results of our study support regional-level studies in finding that the supply of ES varies significantly among land-use types and across spatial scales. The variation in the ES supply derived from land-use change has been assessed over time regionally (Zhao et al. 2004, Helian et al. 2011, Carreño et al. 2012), but few works have compared the supply of multiple ES across multiple land-use types (but see Metzger et al. 2006), and even fewer have done so at a local scale. Our work also supports previous work illustrating that the scale of analysis can alter our understanding of ES provision (Chan et al. 2006, Hein et al 2006), because the cover of land-use types can change the types and quantities of ES provided at different scales. Ecosystem services (ES) that are prominent in a small-scale analysis may be insignificant at a larger spatial scale if the land-use type responsible for their provision is scarce. For example, in our study, habitat provision in riparian forests was very important at the patch scale, but its relevance was almost negligible at the landscape scale because of the small area riparian forests occupy in the floodplain. Likewise, climate regulation by a particular land use may seem unimportant at the patch scale, but become highly relevant when scaled up to the landscape scale because of a large area covered by that land use. Thus, the extent of any single land use at each spatial scale conditions the amount of service provided. Similarly for ES interactions, the spatial scale conditioned the scope of interactions. In our study area, only four interactions between ES remained consistent across spatial scales, highlighting the stability of some interactions. However, the majority of ES interactions changed across scales, indicating that there is no single relevant scale for analyzing ES interactions.

Although Raudsepp-Hearne et al. (2010) suggested that municipalities are a good scale at which to analyze ES interactions, in our multiscale study most ES interactions changed across spatial scales either in significance or in direction (positive vs. negative). This diversity of findings suggests that scientists and decision makers should be aware of the spatial scales at which ES are measured and managed (Daily 2000, Carpenter et al. 2005, de Groot et al. 2010). Although as many ES and interactions as possible should be analyzed for ES and trade-offs assessments, considering at least two spatial scales is key for decision making to assure that repercussions of management actions will stay consistent and will not reverse their effects once upscaled or downscaled. Better still, management actions should be adapted to each specific spatial scale (Aviron et al. 2009). For instance, we encountered difficulties in measuring cultural ES at the patch scale because the information about these services is typically available at the municipality scale. Thus, data had to be downscaled, causing a potential loss of ecologically meaningful data. Moreover, given that many cultural services are influenced by municipal regulations, e.g., access to paths, recreational and fishing areas, establishment of educative panels, etc., it is advisable to measure and manage them at the municipality scale. Trying to manage such services at a large scale, e.g., landscape, may lead to disagreement among government bodies. However, ES such as provisioning services are more amenable to management at the landscape scale despite information being typically available at both patch (per hectare values) and municipal or regional scales, because they greatly influence the landscape features in agroecosystems, and thus, the provision of services at the landscape scale. Therefore, understanding which services respond better to each particular spatial scale is useful for ES management. Matching the appropriate scale to both ES and trade-off analyses is important when payment schemes to protect ES or to encourage sustainable agriculture are to be implemented. Studies carried out in this respect were not able to assure that schemes to enhance ES in agricultural landscapes had the same positive effects locally as regionally or at the national scale (Kleijn et al. 2006). Similarly, field-scale actions did not have the same effects locally as at the landscape scale (Concepción et al. 2012). As we have shown, they argued that this was related to the extent of land-use types under these schemes. This is especially critical when consequences of land-use policies affect millions of people (Carreño et al. 2012), such as the Common Agricultural Policy in the European Union, which incentivized agricultural intensification but has also led to a decrease in biodiversity in agricultural landscapes (Tilman et al. 2002).

We quantified the existence of trade-offs in the supply of ES, as has been posited by many authors (Rodríguez et al. 2006, Nelson et al. 2009, Laterra et al. 2012). The Millennium Ecosystem Assessment (2005) classified ES trade-offs according to their temporal and spatial scales and also depending on their reversibility and the service targeted. Although it is widely recognized that trade-offs arise because of management decisions, which derive from societal needs, values, and preferences, there is little research involving societal values as a potential source of trade-offs (but see Martín-López et al. 2012). We have incorporated societal values as a likely cause of trade-offs between ES. Therefore, we classified ES interactions according to whether they can be driven by biophysical, i.e., ecological, factors or by societal values. In the first case, trade-offs are caused by biophysical interactions between ES and thus are consistent across all land uses (Table 7, example 1) or depend on the land-use type (Table 7, examples 2-4; see also Fig. 2, Table 4). Other trade-offs are caused by management decisions and are thus ultimately derived from societal values (Table 7, example 5).

We expect this classification would be applicable to other ecosystems for trade-offs analysis. Knowledge about the driving forces that provoke trade-offs can improve management for multiple ES. Biophysical trade-offs can often be reduced through specific biophysical management plans within a land-use type. For example, adequate pruning makes raw material production compatible with food production in fruit groves (Table 7, example 1) or with habitat quality in riparian forests (Table 7, example 3). Note that our results exposed the raw material production of fruit groves as a potential value, i.e., neglecting their use as fruit production. Moreover, when simultaneous gain is difficult to achieve, biophysical trade-offs can still be managed for suboptimal but compatible valuable gains (Chan et al. 2006, Trabucchi et al. 2013). Social trade-offs might be managed by considering the mix of land-use types. For example, as shown in Tables 5 and 6, most cultural services can be supplied concurrently with other ES (see also Martín-López et al. 2012).

Because of the high degree of synergies that involve cultural services, the possibility for enhancing the supply of a bundle of ES through promotion of cultural services exists in many municipalities. In our study area and probably in other river floodplains used for agricultural purposes, reopening public paths between the river and the field crops would enhance the supply of a bundle of cultural services yet causing minimal reductions in crop yield. Although synergies are more difficult to identify because significant positive correlations do not always mean that provision of one ES empowers supply of another (Table 7, examples 6-7), exploring in detail which ES or land uses correlate positively or present synergies improves the likelihood that we can enhance the total supply of ES in a targeted area. For example, promoting educational services together with recreational sites will increase the likely use of both services, enhancing the delivery of benefits to society.

Agroecosystems cover a large portion of the terrestrial surface of the Earth. As such, we cannot afford to manage them only for provisioning services because their management will condition the ES provision of the whole system. Rather, they should be managed to deliver multiple ES (Bennett and Balvanera 2006, Harrison et al. 2010), enhancing especially the provision of services of those land uses covering the larger extents of the agroecosystem. To achieve this goal, research on ES compatible with agroecosystems is crucial to improve our understanding of land-use interactions (Trabucchi et al. 2012). A more comprehensive study would likely be required to set the management policies in the area. However, we can already suggest that for the Piedra River, and similar floodplain agroecosystems, a mosaic of habitats comprising productive crops, poplar groves, fruit groves, and restored riparian habitats would increase the supply of ES and the resilience of the floodplain ecosystem, minimizing trade-offs and creating synergies for cultural services, which could ultimately foster rural agritourism, preserve local crops and livestock varieties, promote local products, create jobs, and eventually prevent village depopulation.

CONCLUSION

Each land-use type in the Piedra River floodplain provides ES in unique quantities. Thus, preserving a mixture of land-use types is critical to providing a mixture of services. The amount of each ES supplied in a given area depends on both the per hectare provision of service in a given type of land use and the total area of each land use. The relative importance of each land-use type in supplying ES and the significant interactions among ES change depending on the spatial scale at which measurements and analysis are done. Thus, it is critical to pay careful attention to the scale of analysis considered and its impact on the conclusions. Finally, societal values, as drivers of management decisions, should be studied along with biophysical factors because they likely cause trade-offs between ES and should be considered in management plans. Uncovering the driving forces that provoke trade-offs and exploring which ES or land uses present synergies, such as those shown between cultural services in many municipalities, will enhance land managers’ ability to manage ES bundles.

RESPONSES TO THIS ARTICLE

Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link.

ACKNOWLEDGMENTS

We are grateful to our technicians and students for helping us with field and lab work: A. Barcos, A. Massip, A. Mellado, B. Albero, C. Pastor, E. Lahoz, F. A. Araujo, L. Revuelto, M. Maestro, M. García, P. Errea, P. Sánchez, R. Sorando, and V. Lafuente. Thanks also to A. de Frutos for informatics and statistics help, to M. Zirger for English revision of the manuscript, and to A. de Frutos, C. Ziter and two anonymous reviewers for valuable comments on an earlier version of the manuscript. M. F. L. was granted by CSIC (Spanish National Research Council) under the JAE-predoc program, cofinanced by the European Social Fund. Funding for E. M. B. came from an NSERC Discovery grant.

LITERATURE CITED

Aviron, S., H. Nitsch, P. Jeanneret, S. Buholzer, H. Luka, L. Pfiffner, S. Pozzi, B. Schüpbach, T. Walter, and F. Herzog. 2009. Ecological cross compliance promotes farmland biodiversity in Switzerland. Frontiers in Ecology and the Environment 7(5):247-252. http://dx.doi.org/10.1890/070197

Barral, M. P., and O. N. Maceira. 2012. Land-use planning based on ecosystem service assessment: a case study in the southeast Pampas of Argentina. Agriculture, Ecosystems and Environment 154:34-43. http://dx.doi.org/10.1016/j.agee.2011.07.010

Bates, D., M. Maechler, and B. Bolker. 2012. lme4: linear mixed-effects models using S4 classes. R package version 0.999999-0. [online] URL: http://CRAN.R-project.org/package=lme4

Bennett, E. M., and P. Balvanera. 2007. The future of production systems in a globalized world. Frontiers in Ecology and the Environment 5:191-198. http://dx.doi.org/10.1890/1540-9295(2007)5[191:TFOPSI]2.0.CO;2

Bennett, E. M., G. D. Peterson, and L. J. Gordon. 2009. Understanding relationships among multiple ecosystem services. Ecology Letters 12:1394-1404. http://dx.doi.org/10.1111/j.1461-0248.2009.01387.x

Boletín Oficial de Aragón (BOA). 2012. ORDEN de 25 de enero de 2012, del Departamento de Agricultura, Ganadería y Medio Ambiente, por la que se aprueba el Plan General de Pesca de Aragón para el año 2012. Núm. 19.

Cabell, J. F., and M. Oelofse. 2012. An indicator framework for assessing agroecosystem resilience. Ecology and Society 17(1): 18. http://dx.doi.org/10.5751/ES-04666-170118

Carpenter, S. R., H. A. Mooney, J. Agard, D. Capistrano, R. S. DeFries, S. Díaz, T. Dietz, A. K. Duraiappah, A. Oteng-Yeboah, H. M. Pereira, C. Perrings, W. V. Reid, J. Sarukhan, R. J. Scholes, and A. Whyte. 2009. Science for managing ecosystem services: beyond the Millennium Ecosystem Assessment. Proceedings of the National Academy of Sciences 106:1305-1312. http://dx.doi.org/10.1073/pnas.0808772106

Carpenter, S. R., P. L. Pingali, E. M. Bennett, and M. B. Zurek, editors. 2005. Millennium ecosystem assessment: ecosystems and human well-being: scenarios. Volume 2. Findings of the Scenarios Working Group. Island, Washington, D.C., USA.

Carreño, L., F. C. Frank, and E. F. Viglizzo. 2012. Tradeoffs between economic and ecosystem services in Argentina during 50 years of land-use change. Agriculture, Ecosystems and Environment 154:68-77. http://dx.doi.org/10.1016/j.agee.2011.05.019

Centro de Investigación y Tecnología Agroalimentaria de Aragón/Agrifood Research and Technology Centre of Aragon (CITA). 2008. Estudio sobre la funcionalidad de la vegetación leñosa de Aragón como sumidero de CO2: existencias y potencialidad (estimación cuantitativa y predicciones de fijación). Centro de Investigación y Tecnología Agroalimentaria de Aragón, Zaragoza, Spain. [online] URL: http://www.aragon.es/estaticos/GobiernoAragon/Departamentos/MedioAmbiente/Areas/03_Cambio_climatico/06_Proyectos_actuaciones_Emisiones_GEI/estudio.pdf

Chan, K. M. A., M. R. Shaw, D. R. Cameron, E. C. Underwood, and G. C. Daily. 2006. Conservation planning for ecosystem services. PLoS Biol 4:e379. http://dx.doi.org/10.1371/journal.pbio.0040379

Chen, N., H. Li, and L. Wang. 2009. A GIS-based approach for mapping direct use value of ecosystem services at a county scale: management implications. Ecological Economics 68:2768-2776. http://dx.doi.org/10.1016/j.ecolecon.2008.12.001

Concepción, E. D., M. Díaz, D. Kleijn, A. Báldi, P. Batáry, Y. Clough, D. Gabriel, F. Herzog, A. Holzschuh, E. Knop, E. J. P. Marshall, T. Tscharntke, and J. Verhulst. 2012. Interactive effects of landscape context constrain the effectiveness of local agri-environmental management. Journal of Applied Ecology 49:695-705. http://dx.doi.org/10.1111/j.1365-2664.2012.02131.x

Costanza, R., R. d’Arge, R. de Groot, S. Farber, M. Grasso, B. Hannon, K. Limburg, S. Naeem, R. V. O’Neill, J. Paruelo, R. G. Raskin, P. Sutton, and M. van den Belt. 1997. The value of the world’s ecosystem services and natural capital. Nature 387:253-260. http://dx.doi.org/10.1038/387253a0

Daily, G. C. 2000. Management objectives for the protection of ecosystem services. Environmental Science and Policy 3:333-339. http://dx.doi.org/10.1016/S1462-9011(00)00102-7

DeFries, R. S., J. A. Foley, and G. P. Asner. 2004. Land-use choices: balancing human needs and ecosystem function. Frontiers in Ecology and the Environment 2:249-257. http://dx.doi.org/10.1890/1540-9295(2004)002[0249:LCBHNA]2.0.CO;2

de Groot, R. S., M. A. Wilson, and R. M. J. Boumans. 2002. A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecological Economics 41:393-408. http://dx.doi.org/10.1016/S0921-8009(02)00089-7

de Groot, R. S., R. Alkemade, L. Braat, L. Hein, and L. Willemen. 2010. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological Complexity 7(3):260-272. http://dx.doi.org/10.1016/j.ecocom.2009.10.006

Eigenbrod, F., P. R. Armsworth, B. J. Anderson, A. Heinemeyer, S. Gillings, D. B. Roy, C. D. Thomas, and K. J. Gaston. 2010. The impact of proxy-based methods on mapping the distribution of ecosystem services. Journal of Applied Ecology 47:377-385. http://dx.doi.org/10.1111/j.1365-2664.2010.01777.x

Environmental Systems Resource Institute (ESRI). 2012. ArcGIS Desktop (10.0). Redlands, California, USA.

Felipe-Lucia, M. R. 2012. Social dimension of ecosystem services: the case of river Piedra’s valley. Thesis. Universidad Pablo de Olavide, Sevilla, Spain.

Fox, J. 2003. Effect displays in R for generalised linear models. Journal of Statistical Software 8(15):1-27. [online] URL: http://www.jstatsoft.org/v08/i15/

Goldstein, J. H., G. Caldarone, T. K. Duarte, D. Ennaanay, N. Hannahs, G. Mendoza, S. Polasky, S. Wolny, and G. C. Daily. 2012. Integrating ecosystem-service tradeoffs into land-use decisions. Proceedings of the National Academy of Sciences 109:7565-7570. http://dx.doi.org/10.1073/pnas.1201040109

González del Tánago M., D. García de Jalón, F. Lara, and R. Garilleti. 2006. Índice RQI para la valoración de las riberas fluviales en el contexto de la directiva marco del agua. Ingeniería Civil 143:97-108. [online] URL: http://www2.montes.upm.es/Dptos/DptoIngForestal/Hidrobiolog%C3%ADa/Publicaciones/INDICE_RQI.pdf

Harrison P. A., M. Vandewalle, M. T. Sykes, P. M. Berry, R. Bugter, F. de Bello, C. K. Feld, U. Grandin, R. Harrington, J. R. Haslett, R. H. G. Jongman, G. W. Luck, P. M. da Silva, M. Moora, J. Settele, J. P. Sousa, and M. Zobel. 2010. Identifying and prioritizing services in European terrestrial and freshwater ecosystems. Biodiversity and Conservation 19:2791-2821. http://dx.doi.org/10.1007/s10531-010-9789-x

Hein, L., K. van Koppen, R. S. de Groot, and E. C. van Ierland. 2006. Spatial scales, stakeholders and the valuation of ecosystem services. Ecological Economics 57:209-228. http://dx.doi.org/10.1016/j.ecolecon.2005.04.005

Helian, L., W. Shilong, J. Guanglei, and Z. Ling. 2011. Changes in land use and ecosystem service values in Jinan, China. Energy Procedia 5:1109-1115. http://dx.doi.org/10.1016/j.egypro.2011.03.195

Hothorn, T., F. Bretz, and P. Westfall. 2008. Simultaneous inference in general parametric models. Biometrical Journal 50(3):346-363. http://dx.doi.org/10.1002/bimj.200810425

Hubbart, J. A. 2011. An inexpensive alternative solar radiation shield for ambient air temperature and relative humidity micro-sensors. Journal of Natural and Environmental Sciences 2:9-14. [online] URL: http://www.asciencejournal.net/asj/index.php/NES/article/view/214/214

Hubbart, J. A., T. Link, C. Campbell, and D. Cobos. 2005. Evaluation of a low-cost temperature measurement system for environmental applications. Hydrological Processes 19:1517-1523. http://dx.doi.org/10.1002/hyp.5861

Instituto Aragonés de Estadística. 2011. Padron Municipal of Inhabitants 01/01/2011. Gobierno de Aragón, Zaragoza, Spain. [online] URL: http://www.aragon.es/DepartamentosOrganismosPublicos/Organismos/InstitutoAragonesEstadistica/AreasTematicas/02_Demografia_Y_Poblacion/01_CifrasPoblacion_Y_Censos/01_Padron/ci.01_Cifras_oficiales_poblacion.detalleDepartamento?channelSelected=cb5ca856c66de310VgnVCM2000002f551bacRCRD

Instituto Nacional de Estadística. 2008. Municipal surface. 01/01/2008. Instituto Nacional de Estadística, Madrid, Spain. [online] URL: http://www.ine.es

Kleijn, D., R. A. Baquero, Y. Clough, M. Díaz, J. De Esteban, F. Fernández, D. Gabriel, F. Herzog, A. Holzschuh, R. Jöhl, E. Knop, A. Kruess, E. J. P. Marshall, I. Steffan-Dewenter, T. Tscharntke, J. Verhulst, T. M. West, and J. L. Yela. 2006. Mixed biodiversity benefits of agri-environment schemes in five European countries. Ecology Letters 9:243-254. http://dx.doi.org/10.1111/j.1461-0248.2005.00869.x

Konarska, K. M., P. C. Sutton, and M. Castellon. 2002. Evaluating scale dependence of ecosystem service valuation: a comparison of NOAA-AVHRR and Landsat TM datasets. Ecological Economics 41: 491-507. http://dx.doi.org/10.1016/S0921-8009(02)00096-4

Kreuter, U. P., H. G. Harris, M. D. Matlock, and R. E. Lacey. 2001. Change in ecosystem service values in the San Antonio area, Texas. Ecological Economics 39:333-346. http://dx.doi.org/10.1016/S0921-8009(01)00250-6

Laterra, P., M. E. Orúe, and G. C. Booman. 2012. Spatial complexity and ecosystem services in rural landscapes. Agriculture, Ecosystems and Environment 154:56-67. http://dx.doi.org/10.1016/j.agee.2011.05.013

Millennium Ecosystem Assessment (MEA). 2005. Millenium Ecosystem Assessment. Island, Washington, D.C., USA.

Ministerio de Medio Ambiente Y Medio Rural y Marino. 2009. Mapa de cultivos y aprovechamientos, actualización 2000-2009. Escala 1:50000. Ministerio de Medio Ambiente Y Medio Rural y Marino, Madrid, Spain.

Martín-López, B., I. Iniesta-Arandia, M. García-Llorente, I. Palomo, I. Casado-Arzuaga, D. G. Del Amo, E. Gómez-Baggethun, E. Oteros-Rozas, I. Palacios-Agundez, B. Willaarts, J. A. González, F. Santos-Martín, M. Onaindia, C. López-Santiago, and C. Montes. 2012. Uncovering ecosystem service bundles through social preferences. PLoS ONE 7. http://dx.doi.org/10.1371/journal.pone.0038970

Metzger, M. J., M. D. A. Rounsevell, L. Acosta-Michlik, R. Leemans, and D. Schröter. 2006. The vulnerability of ecosystem services to land use change. Agriculture, Ecosystems and Environment 114:69-85. http://dx.doi.org/10.1016/j.agee.2005.11.025

Mitchell, M. G. E., E. M. Bennett, and A. Gonzalez. 2013. Linking landscape connectivity and ecosystem service provision: current knowledge and research gaps. Ecosystems 16:894-908. http://dx.doi.org/10.1007/s10021-013-9647-2

Montero G., R. Ruiz-Peinado, and M. Muñoz. 2005. Producción de biomasa y fijación de CO2 por los bosques españoles. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaría (INIA), Madrid, Spain.

Moss, T., and J. Monstadt. 2008. Institutional dimensions of floodplain restoration in Europe: an introduction. Pages 3-15 in T. Moss and J. Monstadt, editors. Restoring floodplains in Europe. International Water Association (IWA), London, UK.

Murrell, P. 2005. R graphics. Chapman and Hall/CRC, Boca Raton, Florida, USA.

Nelson, E., G. Mendoza, J. Regetz, S. Polasky, H. Tallis, D. R. Cameron, K. M. A. Chan, G. C. Daily, J. Goldstein, P. M. Kareiva, E. Lonsdorf, R. Naidoo, T. H. Ricketts, and M. R. Shaw. 2009. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment 7:4-11. http://dx.doi.org/10.1890/080023

Nicholson, E., G. M. Mace, P. R. Armsworth, G. Atkinson, S. Buckle, T. Clements, R. M. Ewers, J. E. Fa, T. A. Gardner, J. Gibbons, R. Grenyer, R. Metcalfe, S. Mourato, M. Muûls, D. Osborn, D. C. Reuman, C. Watson, and E. J. Milner-Gulland. 2009. Priority research areas for ecosystem services in a changing world. Journal of Applied Ecology 46:1139-1144. http://dx.doi.org/10.1111/j.1365-2664.2009.01716.x

Pereira, H. M., B. Reyers, M. Watanabe, E. Bohensky, S. Foale, C. Palm, M. V. Espaldon, D. Armenteras, M. Tapia, A. Rincón, M. J. Lee, A. Patwardhan, and I. Gomes. 2005. Condition and trends of ecosystem services and biodiversity. Pages 171-203 in D. Capistrano, C. Samper, M. J. Lee, and C. Raudsepp-Hearne, editors. Millenium ecosystems assessment: ecosystems and human well-being: multiscale assessments. Volume 4. Findings of the Sub-global Assessments Working Group of the Millennium Ecosystem Assessment. Island, Washington, D.C., USA.

Pringle, R. M., D. F. Doak, A. K. Brody, R. Jocqué, and T. M. Palmer. 2010. Spatial pattern enhances ecosystem functioning in an African savanna. PLoS Biol 8(5). http://dx.doi.org/10.1371/journal.pbio.1000377

Power, A. G. 2010. Ecosystem services and agriculture: tradeoffs and synergies. Philosophical Transactions of the Royal Society: Biological Sciences 365:2959-2971. http://dx.doi.org/10.1098/rstb.2010.0143

Quantum GIS Development Team. 2012. Quantum GIS Desktop (1.8.0). Open Source Geospatial Foundation Project, Beaverton, Oregon, USA. http://qgis.osgeo.org

R Development Core Team. 2012. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/

Rathwell, K. J., and G. D. Peterson. 2012. Connecting social networks with ecosystem services for watershed governance: a social-ecological network perspective highlights the critical role of bridging organizations. Ecology and Society 17(2):24. http://dx.doi.org/10.5751/ES-04810-170224

Raudsepp-Hearne, C., G. D. Peterson, and E. M. Bennett. 2010. Ecosystem service bundles for analyzing tradeoffs in diverse landscapes. Proceedings of the National Academy of Sciences 107:5242-5247. http://dx.doi.org/10.1073/pnas.0907284107

Rodríguez, J. P., T. D. Beard, Jr., J. R. B. Agard, E. Bennett, S. Cork, G. Cumming, D. Deane, A. P. Dobson, D. M. Lodge, M. Mutale, G. C. Nelson, G. D. Peterson, and T. Ribeiro. 2005. Interactions among ecosystem services. Pages 431-448 in S. R. Carpenter, P. L. Pingali, E. M. Bennett, and M. B. Zurek, editors. Ecosystems and human well-being: scenarios. Volume 2. Findings of the Scenarios Working Group. Island, Washington, D.C., USA. [online] URL: http://www.unep.org/maweb/documents/document.336.aspx.pdf

Rodríguez, J. P., T. D. Beard, Jr., E. M. Bennett, G. S. Cumming, S. J. Cork, J. R. B. Agard, A. P. Dobson, and G. D. Peterson. 2006. Trade-offs across space, time, and ecosystem services. Ecology and Society 11(1): 28. [online] URL: http://www.ecologyandsociety.org/vol11/iss1/art28/

Rose, S. K., and D. Chapman. 2003. Timber harvest adjacency economies, hunting, species protection, and old growth value: seeking the dynamic optimum. Ecological Economics 44:325-344. http://dx.doi.org/10.1016/S0921-8009(02)00268-9

Scheitlin, K. N., and P. G. Dixon. 2010. Diurnal temperature range variability due to land cover and air mass types in the Southeast. Journal of Applied Meteorology and Climatology 49:879-888. http://dx.doi.org/10.1175/2009JAMC2322.1

Simoncini, R. 2009. Developing an integrated approach to enhance the delivering of environmental goods and services by agro-ecosystems. Regional Environmental Change 9:153-167. http://dx.doi.org/10.1007/s10113-008-0052-x

Swift, M. J., A.-M. N. Izac, and M. van Noordwijk. 2004. Biodiversity and ecosystem services in agricultural landscapes - are we asking the right questions? Agriculture, Ecosystems and Environment 104:113-134. http://dx.doi.org/10.1016/j.agee.2004.01.013

Tallis, H., and S. Polasky. 2009. Mapping and valuing ecosystem services as an approach for conservation and natural-resource management. Annals of the New York Academy of Sciences 1162:265-283. http://dx.doi.org/10.1111/j.1749-6632.2009.04152.x

Tianhong, L., L. Wenkai, and Q. Zhenghan. 2010. Variations in ecosystem service value in response to land use changes in Shenzhen. Ecological economics 69:1427-1435. http://dx.doi.org/10.1016/j.ecolecon.2008.05.018

Tilman, D., K. G. Cassman, P. A. Matson, R. Naylor, and S. Polasky. 2002. Agricultural sustainability and intensive production practices. Nature 418:671-677. http://dx.doi.org/10.1038/nature01014

Tockner K., and J. A. Stanford. 2002. Riverine flood plains: present state and future trends. Environmental Conservation 29(3):308-330 http://dx.doi.org/10.1017/S037689290200022X

Trabucchi, M. 2012. La evaluación de los servicios de los ecosistemas como herramienta para planificar la restauración ecológica de cuencas hidrográficas. Dissertation. Universidad de Zaragoza, Zaragoza, Spain. [online] URL http://zaguan.unizar.es/record/9910/files/TESIS-2012-144.pdf

Trabucchi, M., F. A. Comín, and P. J. O’Farrell. 2013. Hierarchical priority setting for restoration in a watershed in NE Spain, based on assessments of soil erosion and ecosystem services. Regional Environmental Change. 13(4):911-926. http://dx.doi.org/10.1007/s10113-012-0392-4

Trabucchi, M., P. Ntshotsho, P. O’Farrell, and F. A. Comín. 2012. Ecosystem service trends in basin-scale restoration initiatives: a review. Journal of Environmental Management 111:18-23. http://dx.doi.org/10.1016/j.jenvman.2012.06.040

Viglizzo, E. F., and F. C. Frank. 2006. Land-use options for Del Plata Basin in South America: tradeoffs analysis based on ecosystem service provision. Ecological Economics 57:140-151. http://dx.doi.org/10.1016/j.ecolecon.2005.03.025

Zhang, W., T. H. Ricketts, C. Kremen, K. Carney, and S. M. Swinton. 2007. Ecosystem services and dis-services to agriculture. Ecological Economics 64:253-260. http://dx.doi.org/10.1016/j.ecolecon.2007.02.024

Zhao, B., U. Kreuter, B. Li, Z. Ma, J. Chen, and N. Nakagoshi. 2004. An ecosystem service value assessment of land-use change on Chongming Island, China. Land Use Policy 21:139-148. http://dx.doi.org/10.1016/j.landusepol.2003.10.003

Zuur, A. F., E. N. Ieno, N. J. Walker, A. A. Saveliev, and G. M. Smith. 2009. Mixed effects models and extensions in ecology with R. Springer, Amsterdam, the Netherlands. http://dx.doi.org/10.1007/978-0-387-87458-6

Address of Correspondent:
María R. Felipe-Lucia
Instituto Pirenaico de Ecología-CSIC
Av. Nuestra Señora De La Victoria, s/n.
Jaca, Huesca
Spain 22700
maria.felipe.lucia@gmail.com
Jump to top
Table1  | Table2  | Table3  | Table4  | Table5  | Table6  | Table7  | Figure1  | Figure2  | Figure3  | Appendix1  | Appendix2