One Ecosystem :
Research Article
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Corresponding author: Sabine Lange (lange@phygeo.uni-hannover.de), Tim Diekötter (tdiekoetter@ecology.uni-kiel.de)
Academic editor: Joachim Maes
Received: 23 Jun 2023 | Accepted: 04 Sep 2023 | Published: 12 Oct 2023
© 2023 Sabine Lange, Alice Mockford, Benjamin Burkhard, Felix Müller, Tim Diekötter
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Lange S, Mockford A, Burkhard B, Müller F, Diekötter T (2023) As green infrastructure, linear semi-natural habitats boost regulating ecosystem services supply in agriculturally-dominated landscapes. One Ecosystem 8: e108540. https://doi.org/10.3897/oneeco.8.e108540
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Semi-natural linear landscape elements, such as hedgerows, are vital structures within agricultural landscapes that have an impact on ecosystem processes and support biodiversity. However, they are typically omitted from green infrastructure planning, which could lead to significant undervaluing of landscapes and their multifunctionality in terms of ecosystem service supply. Using the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model suite, we tested the effects of additionally including semi-natural linear landscape elements on the model outcomes for crop pollination, nutrient regulation, erosion regulation and water flow regulation ecosystem services supply. The results showed that linear semi-natural landscape elements contribute positively to the landscape’s multifunctionality. Small changes have been identified for water flow regulation, whereas, considering both spatial extent and magnitude of the changes, the greatest changes have been found with respect to the supply of pollination and nutrient regulation. Direct proximity of the linear elements had the greatest effect on ecosystem service supply, in particular with regard to pollination. Based on our results, a more pronounced consideration of semi-natural linear landscape elements as an important element of green infrastructure is advisable.
hedgerow, ecosystem service model, InVEST, scenarios, agroecosystems, GI
Agricultural landscapes are dominated by cultivated areas that are typically interspersed with resource-rich, semi-natural elements, such as fallow fields, field margins, hedgerows or woodlands (
At a national scale, smaller scale Linear Semi-natural landscape Elements (LSE), such as hedgerows, rows of trees, field copses and riparian vegetation, are not typically included in the official national GI network (
LSE have been described as resembling two forest edges standing back to back, characterised by forest species boarded by ecotones on either side (
In adjacent fields, the presence of LSE can alter microclimate characteristics (
The above-described biotic and abiotic characteristics of LSE, therefore, have the potential to significantly impact the supply of ecosystem services such as pollination, nutrient regulation, water flow regulation and erosion regulation (
Pollination | Pollination relates to the transfer of pollen between flower parts and even more between flowers ( |
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Nutrient regulation | Nutrient regulation has been described as the ability and magnitude of an ecosystem to recycle nutrients ( |
Water-flow regulation | Water-flow regulation is a very important regulating ecosystem service that is influenced by landscape configuration and the corresponding land-cover structure ( |
Erosion regulation | The ecosystem service erosion regulation refers to reduced soil loss from the ecosystem ( |
Conventional agricultural practices lead to altered nutrient cycles, with nutrient in- and outputs being out of balance (
In order to optimise the supply of these four regulating ecosystem services, evidence-based approaches are required to inform landscape management and to optimise the implementation of GI measures. Here, we assessed the relevance of LSE as a potentially integral and previously overlooked, part of GI. The objective of the study was to assess the influence of LSE on the simultaneous supply of four ecosystem services in an agriculturally-dominated landscape. More precisely, by applying the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model suite, we tested the changes in ecosystem service supply when semi-natural linear landscape elements were included in the landscape assessment. We hypothesised that, based upon our InVEST model test:
In the following Section (Materials and methods), the study area, as well as the modelling and analysis approach of the four ecosystem services, are briefly outlined. In the subsequent Sections, the results are presented and discussed, respectively. Eventually, the Conclusions are drawn concerning the hypotheses outlined above.
The study area, the Bornhöved Lakes District, is located in the federal state of Schleswig-Holstein in northern Germany, approximately 30 km south of the City of Kiel. With a spatial extent of around 147 km², it includes the municipalities of Belau, Bornhöved, Gönnebek, Kalübbe, Rendwühren, Ruhwinkel, Schmalensee, Stolpe, Tarbek, Trappenkamp and Wankendorf. The local climate is maritime and humid, with an annual precipitation of approximately 823 mm and an approximate mean temperature of 8.9°C (
The four ecosystem services were modelled with the open-source software InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) geospatial model suite (version 3.12.0). The various InVEST models can be used to map and quantify individual ecosystem services and, thereby, identify the direction and magnitude of change in ecosystem service supply (
the corresponding InVEST models were selected:
Each InVEST model requires spatial input data which were generated using the open-source software QGIS 3.6.3. Two landscape scenarios were simulated: A) landscapes without LSE (herein, scenario A) and B) the actual landscapes with LSE (herein, scenario B). The 2018 CORINE Land-Cover dataset (
Distribution of Land-Use/Land-Cover (LULC) classes in the study area in scenario B (pie) and scenario A (pie + bar), i.e. the bar plot presents the allocation of LULC classes in the model runs excluding LSE in the 5% of the area that is covered with LSE in the model runs including LSE (
In addition to the LULC data, the ecosystem service-specific InVEST models require so-called “biophysical tables” as input datasets (Table
Input datasets and constant values (including sources) used per InVEST model: ‘Pollinator Abundance: Crop Pollination’ (CP); ‘Nutrient Delivery Ratio’ (NDR); ‘Seasonal Water Yield’ (SWY); and ‘Sediment Delivery Ratio’ (SDR).
Required data | Data-sets and sources | InVEST model |
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Land-use/land-cover (Scenario A and B, respectively) | Corine Land Cover (CLC_5) 2018 ( |
SWY, CP, NDR & SDR |
Corine Land Cover (CLC_5) 2018 ( |
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Biophysical table (CP) | nesting_availability_index, floral_resources_index based upon |
CP |
Guild table | species, nesting_suitability_index, foarging_activity_index, alpha (average travel distance) and relative abundance based upon |
CP |
Precipitation (monthly) | grids_germany_multi_annual_precipitation_1991-2020 ( |
SWY |
Evapotranspiration (monthly) | grids_germany_multi_annual_evapo_r_1991-2020 ( |
SWY |
DEM | European Digital Elevation Model (EU-DEM), version 1.1 ( |
SWY, NDR & SDR |
Soil group | HYSOGs250m ( |
SWY |
Watershed | European river catchments ( |
SWY, NDR & SDR |
Biophysical table (SWY) | Integer curve number (CN) values for each combination of soil type and LULC ( |
SWY |
Rain events table (monthly) | Proxy values for Kiel (https://de.climate-data.org/) | SWY |
Threshold flow accumulation | 1000 [calibration based upon a comparison between intermediate outcome stream and AX_Gewaesserachse from the DLM250 ( |
SWY, NDR & SDR |
Proportion of upslope annual available local recharge available each month (alpha_M) | 1/12 [InVEST default value] | SWY |
Proportion of upgradient subsidy available for downgradient evapotranspiration (beta_i) | 1 [InVEST default value] | SWY |
Proportion of pixel local recharge available to downgradient pixels (gamma) | 1 [InVEST default value] | SWY |
Nutrient runoff proxy (Scenario A and B, respectively) | Quickflow index [Calculated using InVEST Seasonal Water Yield model run without LSE] | NDR |
Quickflow index [Calculated using InVEST Seasonal Water Yield model run with LSE] | ||
Biophysical table (NDR) | load_n as nitrogen surplus ( |
NDR |
Borselli k parameter (constant) | 2 [InVEST default value] | NDR, SDR |
Subsurface critical length (constant) | 200 [InVEST default value] | NDR |
Subsurface maximum retention efficiency (constant) | 0.8 [InVEST default value] | NDR |
Rainfall erosivity index (R) | R_FAKTOR_RADKLIM_v.2017_002_postproc ( |
SDR |
Soil erodibility (K) | Soil Erodibility (K- Factor) High Resolution dataset for Europe ( |
SDR |
Biophysical table (SDR) | usle_c and usle_p values for each LULC ( |
SDR |
Borselli IC0 parameter, maximum slope length parameter (L) and maximum SDR value (SDRmax) | 0.5, 122, 0.8 [InVEST default values] | SDR |
Each service-specific InVEST model produces a number of outputs in the form of raster layers (herein, service variables; Table
Overview of considered output data (service variables) from the InVEST modelling (
Service variable | Description | InVEST model | Ecosystem service |
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Total pollinator abundance (herein: Pollinator abundance) | The pollinator abundance describes the activity of the pollinators in the study area. It is estimated, based upon the availability of floral resources and the species-specific estimated nesting potential of the landscape. The InVEST model estimates the pollinator abundance for each species. In this study, we only consider the total pollinator abundance across all species. | CP | Pollination |
N total export (herein: Nutrient export) | The nutrient export (here nitrogen only) corresponds to the estimated quantity of the nutrients that eventually reach the stream. It is the sum of the surface and subsurface contributions. | NDR | Nutrient regulation |
Baseflow | The baseflow corresponds to the local amount of precipitation that gradually enters the sub-surface flow. | SWY | Water flow regulation |
Quickflow | The quickflow corresponds to the amount of precipitation that runs off of the land directly, mostly during or shortly after a precipitation event. | SWY | Water flow regulation |
Avoided erosion | The avoided erosion presents the contribution of the vegetation to the reduction of erosion. | SDR | Erosion regulation |
The post-GIS assessment steps related to data processing, quality control, statistical analysis and presentation were performed in R (version R-4.0.4) , mainly using the packages dplyr, plotly and ggplot2. The summary statistics for all variables were calculated and, for each variable, in the context of data quality control, outliers outside of the range of three standard deviations were deleted. Then, the relative share of land area for each quantile supply class and each service variable was determined for our landscape without and with LSE.
Based upon the shifted distribution of the quantile supply classes and general summary statistics, the landscapes were compared for the supply patterns of each considered ecosystem service. The change in ecosystem service supply with LSE in the landscape was calculated for each variable at data points next to LSE (50 m), near to LSE (100 m) and for all data points in the study region.
Finally, to assess the multifunctionality of LSE on agricultural areas and, hence, the potential impact on agricultural production, the change in the mean value of each service variable across non-irrigated arable lands and pastures (agricultural areas) was plotted. To allow a more intuitive comparison with the other service variables, the inverse of the variables quickflow and nutrient export was calculated, i.e. “avoided quickflow” and “avoided nutrient export” (i.e. turning them from an ecosystem disservice into a service).
The spatial distribution of the quantile supply classes differed the greatest between the landscapes of scenario A and B for the ecosystem services nutrient regulation and pollination, i.e. for the variables nutrient export and pollinator abundance (Fig.
Spatial distribution of the quantile supply classes for the service variables:
in scenarios A and B. For means of comparability, for each service variable, a quantile classification based upon the scenario A layer has been applied (background:
Referring to pollination, large patches with very-low to low pollinator abundances were identified in the centre as well as along the southern and northern borders in landscapes under scenario A. Higher pollinator abundance values were, in particular found in and around the forested areas (see Fig.
Distribution of quantile supply classes with respect to the service variables pollinator abundance, avoided nutrient export, avoided quickflow, baseflow and avoided erosion values in scenarios A and B. For means of comparability, for each service variable, the quantile classification based upon the scenario A layer has been applied.
Relative changes in service variables in 50 m (light blue) and 100 m (dark blue) distance to the linear semi-natural landscape elements (LSE) and for the total Landscape (green) for average pollinator abundance, avoided nutrient export, avoided quickflow, baseflow and avoided erosion through the integration of landscape elements.
In terms of nutrient export, the study area was dominated by medium to very high rates previous to LSE inclusion, with the exception of a few patches in the centre and the borders (scenario A). The spatial pattern very roughly followed the spatial distribution of the calculated quickflow values. The few patches with relatively low nutrient export values spatially matched forested areas. Once LSE were included, nutrient export decreased throughout the whole study region (Fig.
In terms of the water flow regulation variable baseflow, very low to low values were found in the northern part of the study area, whereas the southern part was dominated by medium to high values under scenario A. Through the inclusion of LSE in the assessment under scenario B, values along the newly-included LSE changed to high baseflow, however, with little or no change in the surrounding areas (Fig.
Concerning erosion regulation, the avoided erosion (corresponding to soil retention) is characterised by a heterogeneous spatial distribution of the quantile supply classes, whereby the spatial pattern seems to follow the general topography of the region (see Suppl. material
The inclusion of LSE had a net positive effect on ecosystem service supply to agricultural areas (non-irrigated arable lands and pastures). Avoided nutrient export on agricultural grounds displayed a strong positive response to LSE (Fig.
Change in multifunctionality through the integration of linear semi-natural landscape elements (LSE) expressed as relative profiles for average pollinator abundance, avoided nutrient export, avoided erosion, avoided quickflow and baseflow on agricultural grounds (non-irrigated arable lands and pastures).
We showed that considering linear semi-natural landscape elements (LSE) as part of the green infrastructure (GI) increased the modelled multifunctionality of agricultural landscapes. The five per cent of the landscape, assigned to LSE, particularly increased the model results with regard to the supply of the ecosystem services pollination and nutrient regulation, whereas water regulation and erosion control did not respond that much. While recent studies identified InVEST results to be highly dependent on data quality, spatial scale and resolution (
Our results show that the common current exclusion of LSE in national GI planning disregards valuable LSE and their potential to supply ecosystem services. Of the four ecosystem services tested, pollination and nutrient regulation showed strong positive responses to including LSE in the modelled landscape. The area that was positively influenced corresponds not only to the spatial extent of the LSE themselves, but extends beyond their location, on to adjacent agricultural fields. This confirms that the supply of ecosystem services to agriculture is highly dependent on the distribution of LSE, such as hedgerows, in the surrounding landscape (
Historically, hedgerow networks were established to mark boundaries and enclose fields and meadows, rather than for the supply of specific ecosystem functions or services (
Hedgerows are attractive foraging habitats for native bees, especially in early summer (
By reducing the slope gradient and the effective slope lengths of the landscape, LSE may be expected to reduce soil erosion and nutrient runoff. In our study, though, the ecosystem services modelling results with regard to erosion regulation and water flow regulation were only marginally affected by the inclusion of LSE, even though previous results suggest otherwise (
Thus, contrary to our findings, hedgerows are expected to significantly increase both the lateral flow of water, decreasing surface runoff, as well as evapotranspiration, affecting soil water content, especially within close proximity (
It needs to be considered that, in our study, LSE also included wooded strips, isolated trees, trees in line and groups and field copses whereof some are likely less effective at regulating both surface flow and soil moisture content. Even though InVEST provides powerful and relatively transparent means for the quantification and valuation of multiple ecosystem services (
The accuracy and reliability of ecosystem service assessments in general, as well as through InVEST models, are strongly influenced by the quality, resolution and minimum mapping unit (MMU) of available input data (
In line with our study, hedgerows have been proposed to reduce nutrient losses from agricultural land (
With this study, the assessed multifunctionality of LSE has been restricted to four regulating ecosystem services. In order to obtain a more coherent and integral understanding of the functionality of LSE, related ecosystem services synergies and potential trade-offs on the landscape scale, additional ecosystem services need to be assessed. Besides, different methodological approaches, data and models of different complexity and spatio-temporal resolution should be applied. Comparing the results will increase the holistic understanding of LSE at the landscape scale. Furthermore, it will be possible to identify minimum requirements for such assessments with regard to, for example, input data, resolution, complexity, abstraction and stakeholder involvement to ensure reliable results. Additionally, future assessments should integrate an analysis of the condition of the linear semi-natural landscape elements. Information on the condition of the LSE will improve the quality of the assessments and allows more accurate and robust conclusions about the specific functionalities of the assessed structures in the landscape context.
This study addresses the multifunctionality of agriculturally-dominated landscapes and the role of GI, more precisely semi-natural linear landscape elements, in that context. Concluding from the results obtained in this model test, the following can be stated in regard to the research hypotheses:
In order to support sustainable approaches to agriculture, ecological processes and ecosystem functions must be preserved to supply ecosystem services (
This research was funded as part of the BiodivERsA project ‘Integrative Management of Green Infrastructures Multifunctionality, Ecosystem integrity and Ecosystem Services: From assessment to regulation in socioecological systems’ (IMAGINE, funding code: 01LC1611B). We acknowledge financial support by Land Schleswig-Holstein within the funding programme Open Access Publikationsfonds.
Overview of summary statistics for each considered output variable from the InVEST assessment (post outlier removal).
Location of inland water bodies as well as LSE and the calculated landscape’s slope.
Location of LSE in the study area and the threat to erosion by water on agricultural land.
Location of LSE and hydrologic soil groups in the study area.
When a slope is divided by stable structures orientated perpendicular to the gradient (e.g. agricultural paths, hedges, grass strips or field edges) that can divert water or significantly slow down its flow, both the runoff volume and water transport force decrease. This has particular significance for erosion processes on the lower slope, as the entire slope length is no longer effective in causing erosion (