bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 Assessment of the relationships between agroecosystem

2 condition and soil erosion regulating ecosystem service in

3 Northern Germany

4

5

6 Paula Rendon a*, Bastian Steinhoff-Knopp a, Philipp Saggau b, Benjamin Burkhard a, c

7

8

9

10 a Institute of Physical Geography & Landscape Ecology, Leibniz University of Hannover,

11 Hannover, Germany

12 b Institute of Geography, Christian Albrechts University, Kiel, Germany

13 c Leibniz Centre for Agricultural Landscape Research (ZALF) Müncheberg, Germany

14

15

16 *Corresponding author

17 Email: [email protected] (PR)

18

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19 Assessment of the relationships between agroecosystem

20 condition and the ecosystem service soil erosion regulation

21 in Northern Germany

22

23 Abstract

24 Ecosystems provide multiple services that are necessary to maintain human life and

25 activities. Agroecosystems are very productive suppliers of biomass-related provisioning

26 ecosystem services, e.g. food, fibre and energy. At the same time, they are highly dependent

27 on respective ecosystem condition and regulating ecosystem services such as soil fertility,

28 water supply or soil erosion regulation. Assessments of this interplay of ecosystem conditions

29 and services are very important to understand the relationships in highly managed systems.

30 Therefore, the aim of this study is twofold: First, to test the concept and indicators proposed

31 by the European Union Working Group on Mapping and Assessment of Ecosystems and their

32 Services (MAES) for the assessment of agroecosystem condition at a regional level. Second,

33 to identify the relationships between ecosystem condition and the delivery of ecosystem

34 services. For this purpose, we applied an operational framework for integrated mapping and

35 assessment of ecosystems and their services. We used the proposed indicators to assess the

36 condition of agroecosystems in Northern Germany and the provision of the regulating

37 ecosystem service control of erosion rates. We used existing data that are available from

38 official databases for the calculation of the different indicators. We show maps of

39 environmental pressures, ecosystem condition and ecosystem service indicators for the

40 Federal State of Lower Saxony. Furthermore, we identified areas within the state where

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41 pressures are high, conditions are limited, and more sustainable management practices are

42 needed.

43 Despite the limitations of the indicators and data availability, our results show positive,

44 negative and no significant correlations between the different pressures and condition

45 indicators, and the control of erosion rates. Although the idea behind the MAES framework is

46 to show the general condition of an ecosystem, when looking at the relationships between

47 condition and ecosystem services, we identified that not all the indicators - as they are

48 proposed- are suitable to explain to what extent ecosystems are able to provide certain

49 ecosystem services. Further research on other ecosystem services provided by agroecosystems

50 would facilitate the identification of synergies and trade-offs. Moreover, the definition of a

51 reference condition, although complicated for anthropogenically highly modified

52 agroecosystems, would provide a benchmark to compare information on the condition of the

53 ecosystems, leading to better land use policy and management decisions

54 Key words: soil, ecosystem state, ecosystem condition indicators, control of erosion rates,

55 Lower Saxony, agriculture, pressures.

56 1. Introduction

57 Human well-being and activities are strongly dependent on ecosystems, their

58 biodiversity, condition, functionality and capacity to deliver multiple services. Ecosystem

59 condition entails a clear link to ecosystem services and has been defined as the overall quality

60 of an ecosystem unit in terms of its main characteristics that underpin its capacity to generate

61 ecosystem services [1]. Assessing ecosystem condition can help to understand to what extent

62 ecosystems are able to provide services in a sufficient quantity and quality. Some studies have

63 focused on the links between natural capital and ecosystem services [2] and the relationships

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64 between biodiversity and ecosystem services [3]. However, understanding the relationships

65 between ecosystem condition and the provision of ecosystem services is a remaining research

66 gap [4,5].

67 Ecosystem condition has been integrated in international and European Union (EU)

68 policies for setting up sustainability and conservation targets and comprises concepts such as

69 ecosystem state, quality, status, health, integrity and functioning [6]. The Sustainable

70 Development Goals (SDGs), for instance, were adopted by the Member States of the United

71 Nations in 2015, as “a call for action to end poverty, protect the planet and improve the lives

72 and prospects of everyone everywhere” [7]. In particular, for biodiversity, goal 15 aims to

73 protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage

74 forests, combat desertification, and halt and reverse land degradation and stop biodiversity

75 loss.

76 In 2011, the EU adopted the Biodiversity Strategy to 2020 [8] and established six

77 targets to halt the loss of biodiversity and ecosystem services in the EU by 2020. These

78 targets are oriented towards the protection of species and habitats, the maintenance and

79 restoration of ecosystems, sustainable agriculture and use of forests, sustainable fishing and

80 healthy seas, fighting invasive alien species and stopping the loss of biodiversity. The 7th

81 Environmental Action Programme (EAP) was adopted by the EU in 2013 [9] and it reinforces

82 the targets and actions of the Biodiversity Strategy. The EAP aims to protect natural capital,

83 stimulate resource-efficient, low-carbon growth and innovation, and safeguard human health

84 and well-being, while respecting Earth’s limits [9]. Some topics that have been highlighted in

85 both policies and that need further action at EU and national level are the protection of soils

86 and the sustainable use of land, as well as forest resources.

87 The EU has established a dedicated working group on Mapping and Assessment of

88 Ecosystems and their Services (MAES) [10] to support the implementation of Action 5 of

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89 Target 2 of the EU Biodiversity Strategy to 2020. This action requires that all Member States

90 of the EU map and assess the state of ecosystems and their services in their territory, assess

91 the economic value of such services and integrate these values into accounting and reporting

92 systems at the national and EU level [8]. As part of its work, MAES has developed a

93 conceptual framework [11] and suggested a series of indicators to assess the condition of

94 different ecosystem types including agroecosystems in the EU [12]. However, these indicators

95 still need to be tested at EU, national/sub-national and regional level; and links with

96 ecosystem services still need to be further investigated. Our approach in this study is to test

97 the indicators suggested by MAES for environmental pressures, ecosystem condition and the

98 relationships with ecosystem services on a regional level, specifically in agroecosystems in

99 Northern Germany, focusing on the soil erosion narrative.

100 Agroecosystems account today for almost half of the area of land use in the EU [13]. In

101 Germany specifically, more than half of the surface area is used for agriculture [14].

102 Agricultural land provides, on the one hand, multiple ecosystem services, especially biomass-

103 related services such as food, fibre, fodder or energy, which are essential for the maintenance

104 of quality of life [15,16]. On the other hand, agriculture itself is strongly dependent on

105 ecosystem services such as nutrient regulation, water supply, pollination (for selected crop

106 species) or soil erosion regulation [17,18]. Changes in the condition of agroecosystems impair

107 the availability of these services and hence jeopardise human well-being. Environmental

108 pressures such as soil erosion, soil biodiversity decline, soil compaction, organic matter

109 decline, soil sealing, and contamination, together with changing climate and water regimes,

110 degrade these ecosystems [19]. In this regard, the maintenance of good condition of

111 agroecosystems is essential to guarantee their resilience, halt biodiversity loss and preserve

112 the sustainable provision of multiple ecosystem services.

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113 Agroecosystems are strongly modified semi-natural systems that are managed with the

114 purpose of enhanced ecosystem service delivery with a strong focus on provisioning services

115 [20]. These ecosystem service outputs are, at least in conventional farming, strongly based on

116 substantial anthropogenic human system inputs including fertilizer, insecticides, herbicides,

117 energy, labour and machinery use and in some cases also irrigation water [21]. Besides

118 ecosystem service outputs, agroecosystem service delivery has also significant environmental

119 effects in the form of externalities such as greenhouse gas emissions, biodiversity loss or

120 water eutrophication [18].

121 Due to the often long-term human interferences in these systems, there are shortcomings

122 when defining a (natural) reference condition of agroecosystems. Agroecosystem condition

123 cannot only be based on the physical and ecological properties of plants and soils, but must

124 take into consideration human interferences that are characteristic of agroecosystems [16].

125 Agroecosystems are considered to be in good condition when they support biodiversity, when

126 abiotic resources such as water, soil and air are not depleted and when they supply multiple

127 provisioning, regulating and cultural ecosystem services [12]. Nevertheless, the establishment

128 of threshold values to determine whether an agroecosystem is in good or bad condition is still

129 under debate [22]. There is no explicit time reference condition as, for example, before the

130 industrial revolution or other reference times like in other ecosystem types. Besides these

131 temporal issues, also the involvement of multiple stakeholders (farmers, policy makers,

132 planners, consumers, environmental groups), which may have different interests and

133 perceptions about the condition of agroecosystems [23], hampers the reference state

134 definition.

135 This study focuses on the ecosystem service control of erosion rates and takes into

136 account that soil erosion by water is a major problem in soil conservation in the EU [24,25].

137 Soil erosion by water accounts for the largest share of soil loss in Central European

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138 agricultural ecosystems, especially in areas with steep slopes [26]. Unsuitable management

139 activities threaten croplands by increasing the vulnerability of soils to erode [24]. The

140 objective of this study is to conduct an integrated assessment of ecosystems for one

141 exemplary ecosystem service in a specific ecosystem type and by making use of the indicators

142 proposed by MAES for the assessment of ecosystem condition. The study applies an

143 operational framework for integrated mapping and assessment of ecosystem and their services

144 suggested by Burkhard et al [27] in agroecosystems in the Northern German Federal State of

145 Lower Saxony. For this purpose, a stepwise approach is followed, including: i) the

146 identification of the policy objective „healthy soils“ (in our study exemplified by soil erosion

147 regulation) as a theme for the assessment; ii) the identification and mapping of

148 agroecosystems; and iii) the selection, quantification and mapping of indicators of

149 agroecosystem condition.

150 The main aim of this study is to test the feasibility of the indicators proposed by MAES

151 for the assessment of agroecosystem condition at a regional level. Thereby, we hope to

152 contribute to the further methodological development and to increase the applicability of the

153 MAES framework and indicators, rather than focussing on obtaining the most thorough and

154 accurate results for a case study. The experience gained with the indicator application will be

155 relevant for other (also non EU/MAES-related) comparable indicator-based studies in

156 agroecosystems, their condition and ecosystem services.

157 The article is organized as follows: First, we explain the methodological approach used

158 for the integrated assessment. Then we present the evaluation of the environmental pressures,

159 ecosystem condition and the ecosystem service control of erosion rates by showing maps of

160 the different indicators. We also statistically analyse the relationships between ecosystem

161 condition and the control of erosion rates. Then we discuss the main limitations of the

162 indicators and the connection between environmental pressures, ecosystem condition and

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163 ecosystem services and conclude with recommendations for further improvement of the

164 MAES framework and indicators.

165

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166 2. Methods

167 2.1. Study area

168 Lower Saxony is a federal state located in the northwest of the Federal Republic of

169 Germany adjoining the North Sea. The federal state has an area of 47,620 km2 (Fig 1). Its

170 climate is characterized as sub-oceanic with average temperatures ranging from 8.3 to 9.5°C

171 and mean precipitation averages ranging from 654 mm in the south-west to 840 mm in the

172 central and northern parts [28]. Agriculture is the main land use in Lower Saxony with 2.6

173 million hectares (approximately 53.7% of the total territory), of which 1.9 million hectares are

174 arable land, 0.7 million hectares are permanent grassland and around 20,000 hectares are

175 permanent crops [29].

176 More than half of the arable land in Lower Saxony is used to grow cereals (mainly

177 winter wheat and winter barley), the remaining part for fodder crops, oilseeds, or potatoes.

178 The farm sizes are very diverse and range from a few hectares of specialized horticultural

179 businesses to large arable farms with several hundred hectares. On average the farms have a

180 size of 83 ha and about three quarters of all farms keep animals, especially dairy cattle and

181 pigs. Farm type, specialization and size can be interpreted as a function of soil fertility,

182 climate conditions and historical land use strategies [29].

183

184 Fig 1. Agricultural areas in Lower Saxony (data source: CORINE Land Use Land Cover,

185 2012)

186 2.2. Conceptual framework

187 In this study, we used the operational framework that guides integrated mapping and

188 assessment of ecosystems and their services proposed by Burkhard et al [27]. Fig 2 provides a

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189 graphical summary of the framework with its nine steps: (1) theme identification; (2)

190 identification of ecosystem type; (3) mapping of ecosystem type; (4) definition of ecosystem

191 condition and identification of ecosystem services to be delivered by agroecosystems; (5)

192 selection of indicators for ecosystem condition and ecosystem services; (6) quantification of

193 ecosystem condition and ecosystem services indicators; (7) mapping ecosystem condition and

194 ecosystem services; (8) integration of results; and (9) dissemination and communication of

195 results. These steps are described in detail in the next paragraphs.

196

197 Fig 2. Conceptual framework applied for integrated mapping and assessment of

198 agroecosystems and the ecosystem service control of erosion rates (based on Burkhard et

199 al. [27]).

200 2.3. Theme identification: Policy Objective healthy soils

201 (Step 1)

202 The first step of the operational framework refers to the question and theme

203 identification, which must be addressed in the ecosystem assessment so that it is relevant for

204 policy, society, business or science. In this case study, we identified the policy objective

205 maintaining healthy soils. Healthy soils, especially for agriculture, have a high functionality,

206 including high biodiversity, fertility and the capacity to sustainably deliver multiple

207 ecosystem services. These services include food and fibre, climate and water regulation, water

208 purification, carbon sequestration, nutrient cycling and provision of habitat for biodiversity

209 [30]. At the same time, the delivery of other ecosystem services such as water supply and

210 regulation, pollination and soil erosion regulation should not be impaired [31].

211 For this study, we focus on the ecosystem service control of erosion rates. The

212 attributes of agroecosystem condition that affect the delivery of this ecosystem service depend

213 on the condition of the soils and the presence of semi-natural areas within or in the vicinity of

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214 the agricultural fields [32,33]. Additionally, the presence of livestock can affect this service

215 by altering the structural condition of soils [34]. Other condition attributes that affect the

216 provision of the service are certain crop rotations and crop types as well as the state of the

217 landscape in which the agroecosystem is embedded [35]. In this case, the main typologies of

218 environmental pressures, habitat and land conversion, climate change, input nutrients and

219 pesticides, overexploitation, and introduction of invasive species, proposed by Maes et al [12]

220 affect the condition attributes (see Fig 3).

221

222 Fig 3. Synthesis of links between environmental pressures, condition, ecosystem service

223 control of erosion rates and policy objectives in agroecosystems (based on Maes et al [12].

224

225 Healthy soils have been an important element in national and European policies. In

226 Germany, for instance, the objective of maintaining and preserving healthy soils was

227 established in the Federal Soil Protection Act of March 17th 1998 [36] and the Federal Soil

228 Protection and Contaminated Sites Ordinance of July 12th 1999 [37]. Both regulations aim to

229 sustainably secure and restore the soil functions by protecting soils against harmful changes

230 and remediating contaminated sites. At EU level, the European Commission adopted the

231 Thematic Strategy for Soil Protection [38] with the objective of protecting soils across the

232 EU. Although the proposal for a Soil Framework Directive was withdrawn by the

233 Commission in 2014, the 7th Environmental Action Programme (EAP) came into force in

234 2014 [9]. The objective of the EAP is the protection and sustainable use of soils. Under

235 priority objective 1, the EAP states that by 2020 “land is managed sustainably in the Union,

236 soil is adequately protected and remediation of contaminated sites is well underway”. To do

237 so, more efforts are required to reduce soil erosion and increase soil organic matter, as well as

238 to remediate contaminated sites.

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239 2.4. Identification and mapping of the ecosystem type

240 agroecosystems (Steps 2 and 3)

241 The second step of the operational framework refers to the identification of the

242 ecosystem type(s) to be assessed in this study. The ecosystem type is agroecosystems which

243 are “communities of plants and animals interacting with their physical and chemical

244 environments that have been modified by people to produce food, fibre, fuel, and other

245 products for human consumption and processing” [39]. Maes et al.[11] proposed a

246 classification of ecosystem types for MAES, in which cropland and grassland belong to the

247 ecosystem type of agroecosystems. We selected cropland ecosystems in Lower Saxony as a

248 study subject because croplands are a main provider of important ecosystem services such as

249 biomass used as food, fodder or as energy source. Croplands are especially threatened by

250 management and external pressures like droughts or floods related to climate change. Due to

251 management induced bare soils within parts of the year, soils on cropland are especially

252 affected by soil erosion.

253 The third step consists of the mapping of the ecosystem type agroecosystems that was

254 previously identified. For this purpose, we used the CORINE Land Cover Data for the year

255 2012 [40], specifically the CORINE Land Cover type 2. Agricultural areas, which includes:

256 211. Non-irrigated arable land, 221. Vineyards, 231. Pastures, 242. Complex cultivation

257 patterns, and 243. Land principally occupied by agriculture, with significant areas of natural

258 vegetation (see Fig 1).

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259 2.5. Definition of ecosystem condition and identification

260 of ecosystem services delivered by agroecosystems (Step

261 4)

262 The fourth step of the operational framework refers to the definition of ecosystem

263 condition and the ecosystem services delivered. Agroecosystems are usually purposely

264 heavily modified ecosystems [18], which means that it is not feasible to compare their

265 condition against undisturbed natural ecosystems. Table 1 shows the median values and the

266 available reference values used in this study to determine the condition of the agroecosystems

267 in Lower Saxony, based on the selected indicators.

268 We selected the ecosystem service control of erosion rates, because soil erosion is one

269 of the main threats to soils [38] with negative impacts on crop production, water quality,

270 mudslides, eutrophication, biodiversity and carbon stock loss [26]. Soils are the medium on

271 which crops are grown and their functionality is the base for biomass production, storage,

272 filtration and transformation of nutrients and water. Furthermore, healthy soils are important

273 for biodiversity conservation and act as carbon storage pools. Additionally, soils are the

274 platform for human activities, provide raw materials and store geological and archaeological

275 heritage [41]. Therefore, soil degradation will lead to the declines of many ecosystem services

276 [42]. Soil erosion causes the soil surface to decrease and then reduce its thickness. Especially

277 the loss of the humus-rich, fertile topsoil layers leads to a reduction of soil functionality and

278 the capacity to provide ecosystem services. If soil formation is not able to compensate soil

279 losses, soil erosion will threaten sustainable crop production as well as water regulation and

280 filtration capacities [43].

281 Table 1. Indicators used for the assessment of environmental pressures and condition of

282 agroecosystems and the ecosystem service control of erosion rates in Lower Saxony

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Spatial Median Lower Indicator class Indicator Description Units Year Reference value Source resolution Saxony Pressure indicators

Habitat Change in ecosystem extent Change in the area (size) of the % per year Municipality 2016- -0.034 N.A [44] conversion and ecosystem within the years 2016 and 2017 degradation 2017. Climate Mean annual temperature Annual mean of the monthly averaged °C 1000 m 1988- 9.74 N.A [45] mean daily air temperature in 2 m height 2018 above ground Mean annual precipitation Annual sum of monthly precipitation. mm 1988- 765.7 N.A 2018

Drought index Annual mean of drought index after de mm °C-1 1995- 37.78 Very humid (35-55) Martonne. 2018 [46]

Precipitation 10 mm Number of days with precipitation ≥ 10 Number of days 1988- 19.45 N.A mm per year. 2018 Precipitation 20 mm Number of days with precipitation ≥ 20 3.77 N.A mm per year. Precipitation 30 mm Number of days with precipitation ≥ 30 0.91 N.A mm per year. Begin of vegetation period Number of consecutive days of the year. Consecutive days of 1992- 82.68 N.A It indicates the begin of the first spring the year 2018 Summer soil moisture Modelled trend in soil moisture over a % NFK (usable field 1991- 66.82 N.A depth of 60 cm. capacity) 2010

Others Soil erosion Amount of soil loss per hectare in a year t ha-1 per year 50 m 2010 0.13 0 - 3 [47] [48] (Actual soil loss) Loss of organic matter Percentage of soil organic carbon loss Mg C ha-1 per year 1000 m 2014 0.015 N.A [49,50] per year.

Ecosystem condition indicators

Structural Crop diversity Average number of crops in a 10 km Number of crops 50 m 2018 44.41 N.A [51] ecosystem diameter per municipality attributes Density of semi-natural areas Percentage of semi-natural areas % 50 m 2012 13.16 N.A [40] (general)

Share of fallow land in Percentage of arable land that is not % Municipality 2010 0.80 N.A [52] Utilized Agricultural Area being used for agricultural purposes (UAA) within the UAA. Share of arable land in Percentage of land used for the % Municipality 2010 81.32 N.A [52] Utilized Agricultural Area production of crops within the UAA (UAA)

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Spatial Median Lower Indicator class Indicator Description Units Year Reference value Source resolution Saxony Share of permanent crops in Percentage of land used for permanent % Municipality 2010 0 N.A [52] Utilized Agricultural Area crops within the UAA (UAA) Livestock Density Stock of animals (cattle, sheep, goats, LU ha-1 Municipality 2010 0.81 N.A [52] equidae, pigs, poultry and rabbits) converted in livestock units (LU) per hectare of UAA. Structural soil Soil Organic Carbon (SOC) Concentration of topsoil organic carbon. % or gr kg-1 1000 m 2010 2.03 1 - 2 % [53] [54] attributes Soil erodibility Susceptibility of soil to erosion by runoff K factor [t h ha-1 N- 50 m 2010 0.21 N.A [54,55] and raindrop impact. 1] Bulk density Weight of soil per cubic meter t m-3 500 m 2009 1.3 1.6 g cm-3 for sandy and [56,57] sandy loam soils 1.75 g cm-3 for coarse textured soils (with clay content < 17.5%) [53] Ecosystem services indicators

Control of erosion Soil erosion risk Potential soil loss. t ha-1 per year 50 m 2010 0.81 N.A [48] rates Prevented soil erosion Difference of potential and actual soil t ha-1 per year 50 m 2010 0.67 N.A [54] loss Provision capacity Share of mitigation of soil erosion (0 to Dimensionless 50 m 2010 0.85 From 0 (no mitigation) [54] 1) to 1 (complete mitigation)

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284 2.6. Selection of indicators for agroecosystem condition

285 and the ecosystem service control of erosion rates (Step

286 5)

287 The fifth step of the operational framework refers to the selection of the indicators for

288 the assessment of ecosystem condition and ecosystem services. As one of the aims of this

289 study is to test the framework and indicators proposed by MAES for mapping and assessment

290 of ecosystem condition in the EU, we chose the indicators for pressures and condition of

291 agroecosystems presented in the 5th MAES report [12]. We selected the indicators for the

292 ecosystem service control of erosion rates based on existing literature and the frameworks

293 used by Guerra et al. [58] and Steinhoff-Knopp and Burkhard [25].

294

295 2.6.1. Criteria for selecting indicators

296 Ecosystem condition indicators allow to assess the overall quality of an ecosystem and

297 its main characteristics that underpin its capacity to deliver ecosystem services [1]. These

298 indicators, together with information on ecosystem extent and services, constitute the main

299 inputs for integrated ecosystem assessments that analyse the links between ecosystem

300 condition, habitat quality and biodiversity, ecosystem services and the consequences for

301 human wellbeing [27].

302 Maes et al. [12] highlighted the main characteristics that ecosystem condition

303 indicators must have in order to inform policies related to the use and protection of natural

304 resources. First, ecosystem condition indicators need to be aligned with the MAES conceptual

305 framework in which socioeconomic systems are linked with ecosystems through the flow of

306 ecosystem services and the drivers that affect ecosystems. Second, they need to support the

307 objectives of the EU environmental legislation and the objectives of the natural capital

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308 accounts. Third, they need to be policy-relevant, which means that they support EU

309 environmental policies as well as related national policies and other policies. Fourth, they

310 need to be spatially explicit by considering the distribution of ecosystems and their use, and

311 they need to be specific for each ecosystem type. Fifth, they need to contribute to measuring

312 progress/trends against a policy baseline towards different policy targets.

313 For this research purpose which aims to assess the feasibility of the MAES indicators

314 proposed by Maes et al [12] to estimate the condition of agroecosystems in Lower Saxony and

315 to link them specifically with the ecosystem service control of erosion rates, we adopted the

316 following criteria for selecting the indicators to be used in this case study:

317  Relevancy: Indicators must be relevant to the ecosystem service control of erosion

318 rates. This means that there is a clear connection between the condition parameter and

319 the provision of the ecosystem service. This connection was determined based on the

320 authors’ knowledge and evidence in scientific literature.

321  Availability of data at a regional scale: The used data have an appropriate resolution

322 for the study area and are detailed enough to recognize regional features (i.e. spatially

323 explicit or data at the level of municipalities).

324  Quantifiable: Indicators must be quantifiable and data can be compared among

325 municipalities.

326  Reliability: The quantification and monitoring of the indicators are both reliable (i.e.

327 data obtained from officially reported data sets).

328 A total 23 indicators was selected for the assessment, whereof 11 indicators correspond

329 to pressure indicators (including 7 climate indicators), 9 correspond to ecosystem condition

330 indicators and 3 to the ecosystem service control of erosion rates. Table 1 provides detailed

331 descriptions of the indicators, including the spatial resolution, year of data collection and data

332 sources.

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333 2.7. Quantification of indicators (Step 6)

334 The sixth step of the operational framework refers to the quantification of indicators for

335 condition and the ecosystem service control of erosion rates. For this purpose, we conducted

336 a data search in multiple public databases of the EU, Germany and Lower Saxony. In this

337 section, we describe the calculation of each indicator.

338

339 2.7.1. Environmental pressure indicators

340 Change in ecosystem extent was calculated based on the agricultural area data of the

341 municipalities in Lower Saxony for the years 2016 and 2017. These data were obtained from

342 the State Statistical Office of Lower Saxony [44].

343

344 Climate

345 The data of the climate indicators were obtained from the German Weather Service

346 (Deutscher Wetterdienst DWD) [45]. The detailed description of the indicators is provided

347 below (see Table 1 for information on spatial resolution):

348 Mean annual temperature: Corresponds to the annual mean of the monthly averaged

349 mean daily air temperature in 2 m height above ground given in degree Celsius, for the period

350 between 1988 and 2018.

351 Mean annual precipitation: Corresponds to the annual sum of monthly total

352 precipitation given in mm for the period between 1988 and 2018.

353 Drought index: The data of annual drought index (from de Martonne [46]) dMI was

354 calculated with:

푃 355 푑푀퐼 = (푇 + 10) (1)

356 Where T [in degree Celsius] was obtained from temperature grids and P [in mm] from

357 precipitation grids for the period between 1995 and 2018.

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358 Precipitation 10 mm, 20 mm and 30 mm: Corresponds to the number of days with

359 precipitations equal or higher than 10, 20 and 30 mm respectively, averaged for the period

360 between 1988 and 2018.

361 Begin of vegetation period: Corresponds to the consecutive days of the year in which

362 the first spring begins averaged for the period between 1992 and 2018.

363 Summer soil moisture was obtained from the DWD who used the AMBAV model that

364 calculates the evapotranspiration and the soil-water balance for different crops [59]. This was

365 done based on the soil moisture in 60 cm depth under grass derived from selected stations for

366 the period between 1991 and 2010, for the months of June, July and August.

367

368 Soil erosion corresponds to the mean actual soil loss and was calculated with the

369 Universal Soil Loss Equation (USLE), applying the German standard DIN 19708 [60].

370 The loss rates were modelled as raster GIS layers for Lower Saxony in a resolution of 50 m

371 based on the methodology applied by [48]. The USLE was calculated as:

372 퐴푐푡푢푎푙 푠표푖푙 푙표푠푠 = 퐾 × 푅 × 퐿푆 × 퐶 × 푃 (2)

373 Where

374 K is the soil erodibility factor [t h ha-1 N-1] for Germany based on the soil overview

375 maps (Bodenkundliche Übersichtskarten [61]) and the approach of Auerswald & Elhaus [55]

376 R is the rain erosivity factor [N ha-1 per year] for Germany based on mean annual

377 summer precipitations for 1980-2010 from the DWD and the regression equation from

378 standard DIN 19708.

379 LS is the topography factor (length and slope) [dimensionless] based on a 50 m Digital

380 Elevation Model (DEM) and the approach from the DIN 19708.

381 C is the crop management factor [dimensionless] based on crop rotations from culture

382 portions on the level of the municipality for the year 2010.

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383 P is the erosion control practices factor [dimensionless] considered in this study as 1

384 due to lack of detailed data for the resolution we used.

385 The results from the calculations were clipped to the arable land according to the land

386 use type Non-irrigated arable land (2.1.1) of the CORINE Land Cover 2012 [40].

387 Loss of organic matter: This indicator was obtained from the average eroded soil

388 organic carbon dataset calculated for the EU [49]. This was done by applying the CENTURY

389 model, which simulates the carbon and nitrogen dynamics in cultivated or natural systems,

390 coupled with soil erosion [50]. We calculated the average soil organic carbon loss per

391 municipality based on a 1 km raster data set.

392

393 2.7.2. Ecosystem condition indicators

394 Crop diversity was calculated based on the number of field blocks in the year 2018. These

395 data were obtained from the Land Development and Agricultural Support programme of Lower

396 Saxony (LEA) Portal [51] of the Lower Saxonian Ministry of Food, Agriculture and Consumer

397 Protection. We calculated this indicator by estimating the number of crops in a regular 1km

398 raster by applying a moving window analysis with a radius of 5 km. By counting the overlaying

399 field, we added them up and obtained the diversity of crops in a diameter of 10 km around the

400 midpoints of each raster cell. The raster data were combined with the shapes of the

401 municipalities to calculate the average number of crops per municipality.

402 Density of semi-natural areas was calculated based on the CORINE Land Cover data of

403 2012. The land cover classes were selected according to those proposed by García-Feced et al

404 [62]: 2.3.1. Pastures, 2.4.3. Land principally occupied by agriculture, with significant areas of

405 natural vegetation and natural grasslands, the land cover class 2.4.4. Agro-forestry areas is not

406 present in the dataset in Lower Saxony. The data of these land cover classes were combined

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407 with the shapes of the municipalities to calculate the percentage of semi-natural areas in

408 relation to the area of the municipality.

409 The share of fallow land in Utilized Agricultural Area (UAA) was calculated by

410 dividing the number of hectares of fallow land and set aside or decommissioning land over the

411 number of hectares of UAA per municipality. The same approach was used for the calculation

412 of the share of arable land and permanent crops in the UAA. The data for the estimation of

413 these indicators was obtained from the Thünen-Atlas (Collection of agricultural data from

414 Germany) [52,63] for the year 2010.

415 Livestock density was calculated by dividing the number of livestock units over the

416 number of hectares of UAA per municipality, both were obtained from the Thünen-Atlas

417 [52,63] for the year 2010.

418 Soil Organic Carbon was calculated based on the datasets on humus content in the

419 topsoil and the usage-differentiated soil survey map obtained from the Federal Institute for

420 Geosciences and Natural Resources (BGR for its acronym in German) [54]. We used depths

421 from 0 to 10 cm below the soil surface for grassland, pasture and forestry and depths from 0

422 to 30 cm under the soil surface for crop lands. In order to convert the humus content data into

423 soil organic carbon data, we used the following equations:

424

425 퐻푢푚푢푠 = 퐶표푟푔 × 1.72 (3) For mineral soil (van Bemmelen factor) [64]

426 퐻푢푚푢푠 = 퐶표푟푔 × 2 (4) For peat

427

428 Soil erodibility was calculated as the K factor explained before in the USLE equation

429 [48].

430 Bulk density was obtained from the bulk density calculated for the EU [57]. This

431 physical property was derived from soil texture data sets as described by Ballabio et al [56].

21 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

432

433 2.7.3. Ecosystem service indicators

434 The ecosystem service control of erosion rates is a regulating ecosystem service that

435 mitigates the structural impact potential soil loss. In this study, we adapted the conceptual

436 framework for assessing the provision of regulating ecosystem services developed by Guerra

437 et al [58]. Fig 4 is a graphical representation of the adapted framework, where A shows the

438 concept for the assessment of the ecosystem service control of erosion rates and B shows the

439 implementation in this study.

440

441 Fig 4. Conceptual framework applied for the assessment of the ecosystem service control

442 of erosion rates based on Guerra et al.[58].

443

444 Soil erosion risk is the mean potential soil loss defined as the amount of soil that is lost

445 when there is no vegetation covering the ground [25]. Potential soil loss is determined by the

446 USLE factors rain erosivity (R), soil erodibility (K) and topography (LS) as shown in the

447 following equation [48] (also described in section 2.7.1 for the pressure indicator soil

448 erosion):

449 푃표푡푒푛푡푖푎푙 푠표푖푙 푙표푠푠 = 푅 × 퐾 × 퐿푆 (5)

450 The results from the calculations were clipped to the arable land according to the land

451 use type Non-irrigated arable land (2.1.1) of the CORINE Land Cover 2012.

452 The actual control of erosion rates denotes the prevented soil erosion calculated as the

453 difference of potential soil loss (soil erosion risk) and actual soil loss (soil erosion - described

454 in section 2.7.1).

455 Another indicator used to calculate the ecosystem service control of erosion rates is the

456 provision capacity. This is defined as the fraction of the potential soil loss that was mitigated

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457 by the actual service provision. It ranges from 0 to 1, where 0 represents no mitigation and 1

458 complete mitigation and was calculated based on the USLE model results for Lower Saxony

459 (see [25] for more detailed explanation of the approach).

460 The data of these indicators were combined with the shapes of the municipalities to

461 calculate the average value per municipality.

462 2.8. Mapping of indicators (Step 7)

463 This step of the operational MAES framework refers to the spatial visualisation of the

464 ecosystem condition and the ecosystem service control of erosion rates indicators in maps.

465 All the indicators were edited in ArcGIS 10.7 for representation and analysis and were

466 aggregated to the level of the municipalities to facilitate a comparison.

467 2.9. Integration of results (Step 8)

468 The integration of results refers to the analysis of relationships and interactions between

469 the condition of agroecosystems and the supply of the ecosystem service control of erosion

470 rates. This analysis was conducted from two different angles: one assesses the statistical

471 correlations, the other one analyses the spatial distributions and relationships from the

472 compiled maps.

473 For the statistical correlations, seven classes of the ecosystem service control of erosion

474 rates were selected, based on the classification proposed by Steinhoff-Knopp and Burkhard

475 [25] for the potential soil erosion by water on cropland in Lower Saxony. The classes are No

476 to very low (less than 1 t ha-1 per year eroded soil), very low (1 to 5 t ha-1 per year), low (>5 to

477 10 t ha-1 per year), medium (>10 to 15 t ha-1 per year), high (>15 to 30 t ha-1 per year), very

478 high (>30 to 55 t ha-1 per year) and extremely high (≥ 55 t ha-1 per year). We considered the

479 environmental pressures and ecosystem condition indicators per class, which are also used to

480 classify the actual control of erosion rates. We performed the Akaike Information Criterion

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481 (AIC) [65] technique to estimate the likelihood of the different pressure and condition

482 indicators to predict the values of the seven control of erosion rates classes mentioned above.

483 Additionally, we performed the Kruskal-Wallis rank test [66] on the class medians to detect

484 significant differences between the seven classes and the Jonckheere-Terpstra test [67,68] to

485 identify positive or negative relationships between the delivery of the ecosystem service

486 control of erosion rates and the environmental pressures and ecosystem condition. All

487 statistical work was conducted in RStudio (version 1.2.1335) [69].

488 In order to show an integrated overview of the distribution of the ecosystem service

489 control of erosion rates, the potential soil loss, and the pressures and condition in Lower

490 Saxony, we normalized all the variables and standardized them to a 0 to 100 scale to make

491 them comparable. When looking at pressures and condition variables, we considered the

492 presumable effect of each of them based on the supply of the ecosystem service control of

493 erosion rates to do the normalization. The pressure indicators drought index and summer soil

494 moisture, for instance, are thought to have a positive effect on the supply of the ecosystem

495 service. Regarding the drought index (Ia DM) in regions classified as arid (Ia DM<10) and

496 semi-arid (10≤ Ia DM<20) in the index of the Martonne, the protective cover provided by

497 plants against rain splash decreases with increased aridity [70]. This means that the higher the

498 value of the drought index, the lower the erosion risk. Similarly, higher soil moisture

499 maximizes vegetation cover, resulting in the minimization of sediment transport capacity,

500 with important differences between clay and sandy soil textures [71]. On the other hand, the

501 condition indicators fallow land, livestock density and soil erodibility (K factor) have a

502 presumably negative effect on the control of erosion rates. Poor structural stability as well as

503 less plant cover in fallow systems result in an increased erosion risk [72], and a higher

504 percentage of fallow land, indicating areas with bare soil, increases the soil erosion in a

505 specific area. Likewise, the trampling of livestock disturbs soil and loosens it, which makes

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506 the soil easier to remove by agents of transport and therefore increases its erodibility [34].

507 These five indicators were multiplied by -1 in order to take into account the positive or

508 negative effects when normalizing the original data. We then calculated the average of the

509 normalized values for the pressure and condition indicators.

510 In order to spatially visualize the relationships between the indicators, we created maps

511 showing the overlaps of pressures, condition, soil erosion risk and provision capacity. This

512 analysis allowed us to identify how pressures and condition were related to soil erosion risk

513 and the provision capacity of the agroecosystems to control soil erosion.

514

515 2.10. Dissemination and communication of results (Step 9)

516 This step refers to the preparation of maps and other accompanying material for an

517 effective dissemination and communication of the results. According to Burkhard et al.[27]

518 the results must be communicated to decision makers and other stakeholders who might be

519 interested in the them in order to answer the initial question(s) posed in Step 1. However, for

520 this assessment, we have not involved stakeholders and these results have not been

521 communicated.

522 3. Results

523 The results of the assessment are presented in maps showing the distribution of the

524 indicators of environmental pressures, ecosystem condition and the ecosystem service control

525 of erosion rates within Lower Saxony. Other graphs and maps show the integration of results

526 and the relationships between pressures, condition and control of erosion rates (see data per

527 municipality in Supplementary material S1).

25 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

528 3.1. Mapping and assessment of agroecosystem

529 condition in Lower Saxony

530 3.1.1. Pressure indicators

531 Change in ecosystem extent

532 Agroecosystems in Lower Saxony did not experience great changes in extent from the

533 year 2016 to 2017. Most of the municipalities showed changes in agroecosystem extent from

534 0% to +/- 0.6%. The changes were more significant in municipalities such as Solling (district

535 of Northeim), in the south, with an increase from 199 ha to 291 ha (+46%); Rehlingen

536 (district of Lüneburg), in the northeast, from 1441 ha to 2085 ha (+45%); and Osterheide

537 (district of Heidekreis), also in the northeast with an increase from 754 ha to 1000 ha (+33%).

538 On the other hand, there were significant reductions in the size of agroecosystems on the

539 North Sea islands of Baltrum from 134 ha to 56 ha (-58%) and Juist from 1232 ha to 273 ha (-

540 77%), both in the district of Aurich (Fig 5a).

541 Fig 5. Maps of indicators of environmental pressure, ecosystem condition and control of

542 erosion rates in Lower Saxony. (Large maps in Supplementary material S2)

543 Climate

544 Mean annual temperatures in Lower Saxony ranged from 6.7 °C to 10.3 °C, with the

545 lowest temperatures recorded in mountainous areas such as the Harz (district of Goslar) in the

546 southeastern part (Fig 5b).

547 Mean annual precipitation in Lower Saxony ranged from 570 mm in the east to 1344

548 mm in the mountainous region located in the southeastern part. Precipitation in the coastal

549 areas and mountainous regions in the south ranged from 826 mm to 1021 mm (Fig 5c).

550 Drought index or aridity index of the Martonne ranged from humid (28) to extremely

551 humid (78) across the study area. Municipalities located in mountainous regions (Braunlage

552 and Clausthal-Zellerfeld in the district of Goslar) had the lowest aridity together with areas

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553 with continental weather (near the state Saxony Anhalt and some areas close to North Rhine

554 Westphalia) (Fig 5d).

555 The number of days with precipitation higher than 10, 20 and 30 mm was negatively

556 correlated with the amount of rainfall that was recorded. For instance, the number of days

557 with precipitation between 10 and 20 mm ranged from 2 to 42 days, while the number of days

558 with precipitation between 20 mm and 30 mm ranged from 0.6 to 4.6 days (Fig 5e-g).

559 Similarly to other climatic parameters, higher precipitations occurred in mountainous and

560 coastal regions.

561 The begin of the vegetation period in Lower Saxony ranged from 77 to 106 consecutive

562 days of the year in the period between 1992 and 2018 across the different municipalities. The

563 areas with the latest beginning of spring are located in the mountainous regions (Braunlage

564 and Clausthal-Zellerfeld in the district of Goslar), which is related to the prevalence of lower

565 temperatures throughout the year (Fig 5h).

566 Summer soil moisture in Lower Saxony was in line with the precipitation levels

567 showing percentages of plant-available water ranging from 60% in the eastern part to 83% in

568 the southeast (mainly mountainous regions) (Fig 5i).

569 Soil erosion had on average the highest values in the southern mountainous region (56.9

570 t ha-1 per year in the municipality of Wenzen, district of Holzminden), whereas municipalities

571 located in the Lower Saxonian German Plain in the northern half of the state, showed median

572 values below 1.8 t ha-1 per year of (Fig 5j).

573 Loss of organic matter had the highest values in the southern region (0.48 Mg C ha-1 per

574 year, municipality of Braunlage, district of Goslar). Other municipalities in the east and north

575 showed values ranging from 0.06 to 0.1 Mg C ha-1 per year. The median for the whole federal

576 state was 0.02 Mg C ha-1 per year (Fig 5k).

577

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578 3.1.2. Ecosystem condition indicators

579 Crop diversity varied greatly across Lower Saxony. The highest values are located in

580 the northeastern and central areas ranging from 55 to 73 crop species. Whereas the lowest

581 values, below 21 crop species, were recorded on the islands in the northwest and the

582 mountainous area in the district of Goslar in the southeast (Fig 5l).

583 Density of semi-natural areas was higher in the northwest of Lower Saxony, with

584 percentages as high as 91% in the municipality of Ovelgönne (district of Wesermarsch) and

585 more than 87% in Engelschoff (district of Stade), mostly represented in the form of pastures.

586 Almost 40% of the municipalities distributed in the southern and northeastern part have less

587 than 10% of semi-natural areas and feature intensive agriculture, mainly with cereal crops

588 (Fig 5m).

589 The share of fallow land in UAA was relatively low in Lower Saxony, with a median of

590 0.86% for the whole federal state. Almost 60% of the municipalities had a percentage of

591 fallow land below 1.3%, whereas only 41 municipalities had values higher than 6.7%, mainly

592 located in the eastern region (Fig 5n).

593 The share of arable land in UAA was higher in areas of lower density of semi-natural

594 elements, which is in line with theoretical expectations, where most regions with intensive

595 agriculture tend to have low values of semi-natural vegetation [62]. Municipalities with a

596 share of arable land of around 80% were mainly distributed in the east and southwest parts of

597 the federal state, where the production of crops such as grains and wheat was high (Fig 5o).

598 Share of permanent crops in UAA was low in Lower Saxony, with only five

599 municipalities located in the district Stade in the northern part, with a share higher than 80%

600 that correspond mainly to crops such as tree and berry fruits and fruit tree plantations in the

601 fruit growing region “Altes land”. The median share of permanent crops for the federal state

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602 was 0%, since only 175 municipalities out of the 959 in the study have values higher than 0%

603 (Fig 5p).

604 Livestock density was higher in the western part of Lower Saxony with the highest

605 number of livestock units per hectare (LU ha-1) in the districts of Vechta and Cloppenburg

606 with values between 2 and 3.6 LU ha-1. Values smaller than 0.4 LU ha-1 were recorded in the

607 eastern part of the state and on some islands in the northwest. The median livestock density of

608 the federal state was 0.8 LU ha-1 (Fig 5q).

609 Soil Organic Carbon concentrations were high in the northwest of the state with levels

610 of topsoil organic carbon higher than 4% (Fig 5r).This agrees with large peatland areas in

611 Northwestern Germany. The levels were higher than the threshold values between 1 and 2%

612 as estimated by Kibblewhite et al [53]. On the other hand, levels of soil organic carbon in the

613 eastern part of the study area, especially in the district of Wolfenbüttel, were lower than 0.9%,

614 which could be an indication of potential degradation.

615 Soil erodibility or K factor values ranged from 0 to 0.5 t h ha-1 N-1 and showed higher

616 mean values in the southeast of the study area and some municipalities in the northwest (Fig

617 5s). Additionally, the topsoils in these areas had high contents of silt, which makes them

618 highly erodible [24].

619 Bulk density in Lower Saxony ranged from 0.97 t m-3 to 1.53 t m-3 distributed along all

620 the municipalities (Fig 5t). These values were lower than the threshold levels for sandy and

621 sandy loam soils of 1.6 g cm-3 estimated by Huber et al [47].

622

623 3.1.3. Ecosystem service indicators

624 Soil erosion risk showed high values in the southeast part of the study area with values

625 higher than 140 t ha-1 per year, which are considered extremely high according to Steinhoff-

626 Knopp & Burkhard [25]. However, the median value of the entire state was calculated to be

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627 0.8 t ha-1per year, and some municipalities even showed mean values as low as 0.01 t ha-1 per

628 year (Fig 5u).

629 Prevented soil erosion showed a concentration of high values mainly in the southeastern

630 part of the study area (Fig 5v). As previously mentioned, this area also showed high values of

631 potential and actual soil losses. However, the actual soil loss was considerably lower than the

632 calculated soil loss potential, resulting in a high ecosystem service provision in this area.

633 Provision capacity was relatively high in Lower Saxony with values ranging from 0.74

634 to 0.95 across all municipalities (Fig 5w). The mean value of 0.85 indicates that most parts of

635 the study area are effectively protected against soil erosion.

636 3.2. Relationships between agroecosystem condition and

637 control of erosion rates

638 3.2.1. Analysis of the relationships between indicators

639 In order to understand the relationships between agroecosystem condition and supply of

640 the ecosystem service control of erosion rates, we analysed the likelihood of the different

641 indicators (excluding soil erosion, soil erosion risk, and soil erodibility which were partly

642 included in the calculation of the ecosystem service) to predict the classes from no to very low

643 to extremely high mentioned in section 2.9. Our results show that the indicators that best

644 predicted these classes are: loss of organic carbon, followed by mean annual temperature and

645 begin of vegetation period. In contrast, the indicators that had the lowest likelihood to predict

646 the classes were mean annual precipitation and crop diversity (see AIC rank on Fig 6 and

647 Supplementary material S3).

648 Additionally, we analysed the relationships between pressures and ecosystem condition

649 and the ecosystem service control of erosion rates (see Supplementary material S3). Our

650 results show that the rates of control of erosion were slightly higher in areas with increased

30 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

651 ecosystem extent (p < 0.05) (Fig 6a). For the climatic variables, negative, positive as well as

652 not significant correlations were evident. For instance, in our study area, the control of

653 erosion rates was extremely high in areas with lower temperatures (p < 0.05) (Fig 6b). On the

654 other hand, high and extremely high control of erosion rates occurred in areas where variables

655 such as drought index, days with precipitations higher than 20 and 30 mm and begin of

656 vegetation period were high (p < 0.05). However, the relationships between mean annual

657 precipitation, days with precipitation higher than 10 mm, summer soil moisture and the

658 control of erosion rates were not significant (p = 0.2), (p = 0.2), and (p = 0.7) respectively

659 (Fig 6c-i). Higher values of soil erosion and loss of soil organic matter occurred in

660 ecosystems providing higher control of erosion rates (p < 0.05) (Fig 6j-k). In contrast, the

661 density of semi-natural areas and livestock density were low where the control of erosion

662 rates was high or very high (p < 0.05) (Fig 6m and q). Moreover, there was no significant

663 relationship between crop diversity, fallow land, arable land and soil organic carbon with the

664 control of erosion rates (p = 1) (Fig 6l, n, o-r). The condition indicators soil erodibility, bulk

665 density, soil organic carbon and the ecosystem service indicators soil erosion risk and

666 provision capacity showed a positive relationship with the control of erosion rates (p < 0.05)

667 (Fig 6s-v).

668

669 Fig 6. Relationships between the indicators of environmental pressures and condition,

670 and the ecosystem service control of erosion rates.

671

672 3.2.2. Overlaps between environmental pressures, ecosystem condition,

673 soil erosion risk and ecosystem service provision capacity

674 Fig 7 shows the spatial distribution of environmental pressures and condition in relation

675 to the ecosystem service provision capacity in Lower Saxony. Fig 7a shows the

31 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

676 superimposition of the maps of the normalized values of condition and provision capacity.

677 Areas with high provision capacity and high condition levels (darker colours on the right top

678 corner) were mainly located in the northwestern and central regions. High provision capacity

679 was also found in municipalities with medium condition levels located mainly in the southern

680 and eastern regions (dark blue). High provision capacity was not necessarily associated with a

681 high level of ecosystem condition in municipalities such as the Harz in the southeast (light

682 green). However, it is important to highlight that this area is mostly covered by mountainous

683 forest and is part of a national park. On the contrary, some municipalities in the northwest

684 showed low provision capacity, but medium condition levels (light blue). No municipalities in

685 Lower Saxony were found to have low provision capacity and low condition levels.

686 The spatial distribution of provision capacity and pressures shows that most of the study

687 area had medium levels of pressures and medium or high provision capacity (darker blue

688 colours) (Fig 7b). The highest provision capacity occurred in the central and northwestern

689 regions where the pressures had a medium level (dark green). Low provision capacities and

690 medium pressure levels occurred in the northwest, especially in some municipalities of the

691 districts of Cuxhaven, Rotemburg (Wümme) and Wesermarsch.

692

693 Fig 7. Spatial representation of the overlap between environmental pressures and

694 condition, and ecosystem service provision capacity. (a) Overlap between provision

695 capacity and condition. (b) Overlap between provision capacity and pressures. Darker areas

696 represent high provision capacity and high level of condition in (a), or high provision capacity

697 but high pressures in (b). Lighter areas represent lower provision capacity and low level of

698 condition in (a) or lower provision capacity and low pressures in (b). (Large maps in

699 Supplementary material S2).

700

32 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

701 Fig 8 shows the spatial distribution of soil erosion risk, environmental pressures and

702 condition in relation to the ecosystem service provision capacity in Lower Saxony. Fig 8a

703 shows the superimposition of the maps of the normalized values of soil erosion risk, condition

704 and provision capacity. Areas with high provision capacity, high condition levels and low

705 erosion risk (top of the triangle) were mainly located in the northwestern region. High

706 provision capacity was also found in municipalities with medium condition levels and low

707 erosion risk located mainly in the western region and (to a lesser extent) in the northeastern

708 (blue colour on the right side of the triangle) and the south (grey colour on the right side of the

709 triangle). Medium provision capacity, condition and erosion risk were found in the district of

710 Holzminden in the south (grey colour in the middle of the triangle). On the other hand, high

711 provision capacity was evident in areas with medium-low condition levels and medium

712 erosion risk (plum colours on the right side of the triangle). These mismatches were evident in

713 the northwestern and western regions. Medium provision capacity, low condition levels and

714 high erosion risk (tan colour at the bottom of the triangle) were evident in the southern region,

715 especially in the municipality of Wenzen, also in the district of Holzmiden.

716 The spatial distribution of provision capacity, pressures and condition shows that most

717 of the study area had medium levels of pressures, medium condition levels and medium or

718 high provision capacity (grey colours in the middle and on the right side of the triangle) (Fig

719 8b). The highest provision capacity was evident in the districts of Northeim and Goslar, but

720 the condition levels in these areas were low and the pressures were medium. The lowest

721 provision capacity occurred in the northwestern region where the pressures had medium

722 levels and condition had high levels (green colour on the left side of the triangle).

723

724 Fig 8. Spatial representation of the overlap between environmental pressures, condition,

725 soil erosion risk and ecosystem service provision capacity. (a) Overlap between soil

33 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

726 erosion risk, condition and provision capacity. (b) Overlap between pressures, condition and

727 provision capacity. The colours and dots in the triangle show the distribution of the

728 municipalities across the different indicators in percentage. Darker areas represent medium

729 values of all the variables. Dots close the lower right corner indicate high values of provision

730 capacity. Dots close to the lower left corner indicate high erosion risk in (a) and high

731 pressures in (b). Dots close to the upper corner indicate high condition levels. (Large maps in

732 Supplementary material S2).

733 4. Discussion

734 The analysis based on the operational MAES framework proposed by Maes et al [12]

735 and the complied maps provide good results in regard to the relationships between ecosystem

736 condition and soil erosion regulating ecosystem service in Northern Germany. In the

737 following, we will discuss the limitations of the applied indicators and provide

738 recommendations for improved applications.

739 4.1. Limitations of the indicators for ecosystem

740 condition and control of erosion rates

741 4.1.1. Environmental pressures indicators

742 Change in ecosystem extent was calculated at the municipality level, between the years

743 2016 and 2017 since there was no official statistical data available at this level for other time

744 periods. This poses some limitations when assessing trends, because two years do not provide

745 enough information to draw clear conclusions. Additionally, the indicator, as it is proposed

746 here, only shows the degree of change, but not the drivers of change, so when it comes to

747 comparison with an ecosystem service such as control of erosion rates, the mere percentage

748 does not indicate whether the change is positive or negative for the provision of the service.

34 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

749 The analysis of the causes of change is important to understand the relationships between

750 ecosystem condition and ecosystem services, but it was beyond the scope of this study, that

751 aimed at testing the feasibility of the already proposed indicators (from the MAES working

752 group) to assess ecosystem condition and to relate them to ecosystem services.

753 Climate parameters were calculated based on climate data for Germany for at least 30

754 years whenever they were available. However, there are some uncertainties with these data,

755 and also other data used in the study, caused by the different methods of obtaining them as

756 well as interpolations and missing or erroneous observations. In addition, for the climatic

757 variables, the measurement network has changed over time and this affects the comparison of

758 grid fields for the different years [45]. Another possible limitation of the indicator climate

759 parameters is that the MAES framework does not provide guidelines about which specific

760 parameters should be used to assess climate change. For this reason, we selected the most

761 relevant indicators based on the possible influence on the ecosystem service control of erosion

762 rates, which may seem arbitrary when looking at the general condition of the ecosystem or

763 when making comparisons with other ecosystem services other than control of soil erosion,

764 but a valid approach to generate data on soil erosion rates on the regional scale of this case

765 study.

766 Soil erosion was calculated for arable land only, without taking grasslands and forests

767 into account. Additionally, this calculation was made only for water erosion, without

768 including wind erosion, which is a major problem in the northern part of Lower Saxony,

769 therefore, the actual soil erosion might be higher than the results showed in this study.

770 Moreover, the calculation of soil erosion was made using the USLE equation that has some

771 limitations, including the underestimation of the impact in thalwegs and gully erosion [43].

772 Furthermore, we used agricultural statistics and common assumptions about the effects of

35 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

773 management practices and conservation measures for the estimation of soil erosion [48]

774 which is less precise and less explicit than using detailed monitoring data [25].

775 Loss of organic matter values were obtained from the ESDAC, which was considered a

776 useful and sufficient approach for our study area. However, as these values were originally

777 calculated for the continental scale of Europe, some uncertainties exist regarding the accuracy

778 of the results for the regional scale. Although the application of the CENTURY model at

779 regional levels is technically feasible, there is still a lack of input data [50]. The collection and

780 processing of these data would help to improve the accuracy of the results. However, it is very

781 time-consuming and out of the scope of this study.

782

783 4.1.2. Ecosystem condition indicators

784 Crop diversity was estimated based on the number of crop species in a diameter of 10

785 km (see Section 2.7.2). Although this calculation differs from the one suggested in the MAES

786 framework (N° of crops/10 km × 10 km), it is also an approximation that reflects crop

787 diversity. However, similar to the indicator change in ecosystem extent, this indicator does not

788 provide sufficient information to determine whether this number is favourable or not when

789 comparing it with the ecosystem service control of erosion rates. For this, an indicator such as

790 the types of crops in a specific area would be more informative, since some cultures are more

791 prone to soil erosion than others. However, it was not calculated in this study, since our aim

792 was to test the feasibility of the proposed indicators.

793 Density of semi-natural areas was calculated in regard to the area of each municipality

794 and not specifically in regards to the area of the agricultural land. This could overshadow the

795 results since we did not determine the exact location of these elements, but instead estimated

796 their proportion within the municipalities. Additionally, the lack of suitable metrics prevented

797 us from calculating the shares of some semi-natural elements such as semi-natural grasslands,

36 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

798 hedgerows and buffer strips that could have provided a different picture when it comes to

799 identifying the role of semi-natural vegetation in the provision of ecosystem services such as

800 control of erosion rates.

801 The indicators share of fallow land, share of arable land and share of permanent crops

802 in utilized agricultural areas as well as livestock density were calculated based on official

803 statistical data at the level of the municipalities. However, important factors such as the

804 duration and the management of the fallow land can show different results regarding soil

805 erosion in comparison to cultivated lands [72]. Nonetheless, the results could have been more

806 precise and comparable with other indicators, if spatially explicit data were available for the

807 study area. Furthermore, the bare number of livestock units per hectare does not provide

808 sufficient information about the possible impact of livestock on the provision of ecosystem

809 services, because the different types of livestock as well as their management (e.g. landless

810 production systems vs grassland-based) are not assessed by this indicator.

811 Soil Organic Carbon was calculated based on spatially explicit data on humus content

812 in the topsoil and then using common conversion factors to obtain the concentration of soil

813 organic carbon. These spatially explicit results were upscaled to the level of the municipalities

814 in order to allow for the comparison between indicators. However, as with other indicators,

815 this leads to uncertainties in the results, since the soil characteristics, in this case soil organic

816 carbon, are not homogeneously distributed within the area of the municipalities.

817 Soil erodibility or K factor of the USLE showed the same limitations as the calculated

818 soil erosion described before. Furthermore, upscaling these values to the level of the

819 municipalities can increase uncertainty, taking into consideration that the contents of silt,

820 sand, clay and organic matter, as well as other parameters needed to calculate the K factor,

821 usually vary within short distances and these generalizations can lead to less accurate results.

37 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

822 Bulk density values were obtained from the ESDAC, which was considered a useful and

823 sufficient approach for our study, as well as for the indicator loss of organic matter mentioned

824 before. However, bulk density shows limitations similar to the indicator loss of organic

825 carbon as these values were also originally calculated for a continental scale. The processing

826 of available regional data would improve the accuracy of the results, but this would be a very

827 complex and time-consuming approach. It is also important to take into account the high

828 annual variability of this indicator, which can be another source of uncertainty in this type of

829 studies.

830

831 4.1.3. Ecosystem service indicators

832 The ecosystem service indicator potential soil loss (erosion risk) was calculated

833 assuming that the whole arable land is bare soil, taking into account only natural soil erosion

834 by water. The results aggregated to the municipalities provide an approximate risk of erosion

835 and could show a general picture of the ecosystem condition, when combined with other

836 indicators.

837 As mentioned in Section 3.1.3, low values of prevented soil loss occurred also in areas

838 with low soil erosion risk, but this does not necessarily mean that the service supply is low.

839 This highlights that only calculating the prevented soil loss is insufficient to determine the

840 actual ecosystem service supply [25].

841 The provision capacity indicator, which reflects the proportion of the potential soil loss

842 that is mitigated by the ecosystem service control of erosion rates, allows to identify the

843 service supply and to possibly assess different management practices. However, as with other

844 indicators, provision capacity was upscaled to the municipality level and some aspects - e.g.

845 the presence and distance to water courses, relief characteristics like thalwegs, presence of

846 tramlines and wheel tracks, as well as management measures, which affect the provision

38 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

847 capacity [24,73] - could not be identified and hence the results are less accurate than they

848 would be with spatially explicit data.

849 4.2. Relationships between agroecosystem condition

850 and control of erosion rates

851 Since the adoption of the EU Biodiversity Strategy to 2020, the mapping and

852 assessment of European ecosystems and their services has been increasing [74–76]. However,

853 understanding the interdependencies between biodiversity, ecosystem functioning and

854 ecosystem services is still a major challenge [5,6]. Although, several studies provide evidence

855 of the positive relationships between biodiversity, natural capital and ecosystem services

856 [2,77], there is no consensus on what these links are and how they concretely operate [78].

857 Although we were not able to establish the causalities among the indicators with the

858 correlation analysis, we could observe that some indicators are strongly related. Almost all the

859 environmental pressure indicators are strongly related to the ecosystem service control of

860 erosion rates, except for the indicators change in ecosystem extent, mean annual

861 precipitation, days with precipitation equal or higher than 10 mm and summer soil moisture.

862 Regarding the ecosystem condition indicators, we identified that five out of nine condition

863 indicators are strongly related to control of erosion rates. The four indicators that do not show

864 a strong correlation are crop diversity, share of fallow and arable land, and soil organic

865 carbon. As expected, there is a positive correlation between the ecosystem service indicators

866 potential soil loss and provision capacity and the control of erosion rates. It must be noted

867 that this analysis must be perceived with caution, considering the limitations of each of the

868 indicators mentioned above.

39 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

869 4.3. Recommended land management measures to

870 reduce soil erosion

871 Based on the overlaps presented in Section 3.2.2., we identified municipalities as

872 priority areas in which the risk of soil erosion is medium or high, the provision capacity is low

873 and the condition levels are low. Our data enable the identification of these problematic

874 municipalities located in the district of Northeim in the southern region of Lower Saxony. For

875 these areas, it is important to implement measures to reduce the impact of pressures, improve

876 the ecosystem condition and soil conservation. In addition, analyses on local scale must be

877 carried out and measures must take into account site-specific characteristics such as soil, crop

878 varieties, soil degradation and farming practices [19]. When looking at the types of crops in

879 Northeim, we identified cereals, grasslands, oilseeds, pastures for extensive grazing, and

880 fodder plants (maize) as the main agricultural land uses. These crops may certainly have

881 effects on soil degradation and erosion problems due to excessive tillage and crop residue

882 removal [43]. Therefore, the implementation of conservation farming is essential to solve

883 these problems because it can reduce soil erosion by ensuring the protection of the soil surface

884 with residue retention and increased water infiltration.

885 The techniques applied in conservation farming include permanent soil cover with crop

886 residues, which protects the ground surface and provides organic material, thereby improving

887 soil quality. Other techniques are the growth of diverse crop species in the same field and crop

888 rotation, especially crops such as legumes and grasses [34]. Conservation farming also

889 involves minimum soil disturbance that has positive effects on biotic soil activity and leads to

890 increased stability of soil aggregates [35]. Another practice that could be applied to guarantee

891 soil conservation in vulnerable areas is agroforestry. This practice consists of the integration

892 of trees with animals or crops or both, in order to preserve the fertility and structure of the soil

893 through the fixation of nitrogen and the return of organic matter to the soil [34]. All these

40 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

894 measures have an impact on the condition of soils and hence in the condition of

895 agroecosystems, therefore, indicators that address these measures should be taken into

896 account. The indicators proposed by the MAES working group, as they are implemented in

897 this study, are not able to fully address agroecosystem condition relevant for soil protection

898 and soil erosion prevention. Indicators that analyse the effect of crop species on soil erosion,

899 the use of cover crops and other soil conservation measures should be included in the list of

900 proposed indicators.

901 4.4. Potential for MAES/policy implementation

902 The normalization presented in Section 2.9 and Figs 7 and 8 aimed to facilitate the

903 comparison between indicators and to visualize the relations between ecosystem condition,

904 environmental pressures, erosion risk and provision capacity. It is important to highlight that

905 these representations are somewhat arbitrary and possibly other combinations and overlaps of

906 indicators could provide different results. Furthermore, the results presented here come from a

907 methodological study, aiming at testing an existing framework and respective indicators.

908 These results do not yet have the potential to be used to make policy decisions or implement

909 the measures described above to improve the condition or reduce the pressures on

910 agroecosystems. At least not without more detailed, and if possible, spatially explicit data,

911 including more complete information on types and intensities of management practices. The

912 maps presented in this study are intended to provide a general idea of the environmental

913 pressures, ecosystem condition, soil erosion risk and the provision capacity of the ecosystem

914 service control of erosion rates in Lower Saxony and to raise awareness of areas where

915 special attention should be paid to avoid or mitigate ecosystem degradation.

916 Composite indicators can be used to provide insights on environmental condition, as

917 well as sustainability, quality of life, and economy [79]. Such indicators have been useful in

41 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

918 policy analysis and public communication because they seem to be easier for the general

919 public to interpret [80]. However, we did not develop a composite indicator for three main

920 reasons: First, it could add an extra ambiguity to the results. Second, because the suggested

921 indicators would not be sufficient to build a trustworthy index, since threshold levels that

922 would help to determine the overall condition have not been defined for all the indicators.

923 Third, it was not the aim of the study to come up with a composite indicator, but to test the

924 ones already proposed.

925 5. Conclusions

926 This is the first study testing the MAES framework and indicators for the assessment of

927 the condition of agroecosystems in a regional scale case study. Furthermore, it is also an

928 analysis of the relationships between ecosystem condition and the provision of a selected

929 ecosystem service, specifically control of erosion rates. This assessment is useful to identify

930 the suitability of these indicators, check the data availability for respective indicator

931 quantification and to describe ecosystem condition in a regional scale.

932 Although we were not able to establish clear causalities among the indicators, our

933 results identified positive, negative and no significant correlations between the different

934 pressures and condition indicators, and control of erosion rates despite the limitations of the

935 indicators and data availability. The idea behind the MAES framework is to show the general

936 condition of an ecosystem in the context of ecosystem services supply. However, when

937 looking at the relationships between ecosystem condition and ecosystem services, we

938 identified that not all proposed indicators are suitable to explain to what extent

939 agroecosystems are able to provide certain ecosystem services. Condition indicators on crop

940 management and soil conservation measures, which are directly linked to the ecosystem

941 service control of erosion rates, are missing in the list of indicators proposed in the MAES

42 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

942 framework. Additionally, if indicators are to be applied in national or regional scale studies, it

943 is important to consider that trend and high resolution data are not always available in

944 sufficient quality and resolution, which may undermine the results and hence their

945 comparability with other regions.

946 A further aspect to consider in future research is the assessment of other ecosystem

947 services/ecosystem services bundles provided by agroecosystems. This would facilitate the

948 identification of synergies and trade-offs, both between ecosystem services and between

949 ecosystem condition parameters that may have a different degree of influence on ecosystem

950 services. Moreover, a clearer definition of reference conditions, although complicated for

951 agroecosystems, is important to provide more accurate information on the condition of the

952 ecosystem, which should lead to better policy and management decisions.

953 Acknowledgements

954 This study is partly based on the outcomes of the Lower Saxonian soil erosion monitoring

955 programme funded by the Lower Saxonian State Authority for Mining, Energy and Geology

956 (LBEG). We wish to thank the staff of the Joint Research Centre of the European

957 Commission who gave conceptual support. We also wish to thank Tobias Kreklow and Jan

958 Bierwirth for their collaboration in processing the data and Angie Faust for checking the

959 language of the manuscript.

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1186 Supplementary material

1187 S1. Indicators per municipality.

1188 S2. Maps.

1189 S3. Results of tests per indicator.

52 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.26.116285; this version posted May 26, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.