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Ecological Indicators 75 (2017) 101–110

Contents lists available at ScienceDirect

Ecological Indicators

j o ur na l ho me page: www.elsev ier .com/ locate /eco l ind

apping and analysing historical indicators of ecosystem services inermany

ndreas Dittricha,∗, Henrik von Wehrdenb,c, David J. Absonb, Bartosz Bartkowskid,nna F. Corda, Pascal Fustb, Christian Hoyera, Stephan Kambache,f, Markus A. Meyera,ita Radzevičiūtė g,h, Marta Nieto-Romeroi, Ralf Seppelta,j, Michael Beckmanna

UFZ - Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, GermanyLeuphana University Lüneburg, Faculty of Sustainability, Scharnhorststr. 1, 21355 Lüneburg, GermanyLeuphana University Lüneburg, Centre of Methods, Scharnhorststr. 1, 21335 Lüneburg, GermanyUFZ - Helmholtz Centre for Environmental Research, Department of Economics, Permoserstr. 15, 04318 Leipzig, GermanyUFZ - Helmholtz Centre for Environmental Research, Department of Community Ecology, Theodor-Lieser-Str. 4, 06120 Halle, GermanyMartin-Luther-University Halle-Wittenberg, Department of Biology/Geobotany and Botanical Garden, Am Kirchtor 1, 06108 Halle (Saale), GermanyUniversity of Leipzig, Faculty of Biosciences, Pharmacy and Psychology, Institut of Biology – Molecular Evolution and Animal Systematics, Talstr. 33, 04103eipzig, GermanyVilnius University, Faculty of Natural Sciences, Department of Zoology, M.K.Čiurlionio str. 21/27, LT-03101, Vilnius, LithuaniaUniversity of Aveiro, Department of Social, Political and Territorial Sciences, Campus Universitario de Santiago, 3810-193 Aveiro, PortugalMartin-Luther-University Halle-Wittenberg, Institute of Geoscience and Geography, 06099 Halle (Saale), Germany

r t i c l e i n f o

rticle history:eceived 15 June 2016eceived in revised form2 September 2016ccepted 3 December 2016vailable online 26 December 2016

eywords:ultural landscapesot spotsandscape identityiterature reviewpatial analysisraditional land-use systems

a b s t r a c t

In recent ecosystem service studies, historical data have gained importance as basis for analysing tem-poral trends and for adapted land management strategies; however, the total number of such studiesremains small. Contributing to recent efforts, the primary objective of this study was to assess localecosystem service products historically used in Germany and to link their distribution patterns to envi-ronmental gradients and traditional land-use systems. From maps and detailed regional descriptions ofregionally distinct historic farmsteads, building materials used and village types we extracted informa-tion on ecosystem service products appropriated in 1950 and before. A spatial model was used to test thederived ecosystem service diversity against topo-climatic conditions. Regional service richness was fur-ther compared to the type of traditional land-use system (i.e. focus on crops, focus on livestock or mixedsystems). We were able to identify hot spots of historical ecosystem service provisioning in Northern andSouthern Germany, whereas significantly lower service numbers were recorded in Eastern Germany. Thestrong spatial differences in the diversity of historical service products could be explained best by (high)precipitation during the vegetation period. Furthermore, traditional livestock keeping, which relied onvarious fodder sources and fertilisation techniques to improve poor soil quality, and mixed systemsmostly co-occurred with higher regional ecosystem service richness. The baseline of historical ecosys-tem service provisioning analysed here aids our understanding of current land-use patterns in Germany.

Furthermore, a change of perception for specific landscape elements became apparent from our analyses.For example, hedges planted to separate livestock and to provide fuel in the past are today appreciated asimportant elements for biodiversity conservation. Furthermore, our study helps to preserve knowledgeabout locally sourced ecosystem services thereby increasing the understanding of cultural landscapeswhich may help to maintain their remnants.

© 2016 Elsevier Ltd. All rights reserved.

∗ Corresponding author.E-mail address: andreas.dittrich@ufz.de (A. Dittrich).

ttp://dx.doi.org/10.1016/j.ecolind.2016.12.010470-160X/© 2016 Elsevier Ltd. All rights reserved.

1. Introduction

In recent ecosystem service (hereafter ES) studies, historical

data have gained increasing importance in determining trade-offs and synergies among multiple ES and as basis for adaptingland management strategies (Morán-Ordóñez et al., 2013; Renard

dx.doi.org/10.1016/j.ecolind.2016.12.010http://www.sciencedirect.com/science/journal/1470160Xhttp://www.elsevier.com/locate/ecolindhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.ecolind.2016.12.010&domain=pdfmailto:andreas.dittrich@ufz.dedx.doi.org/10.1016/j.ecolind.2016.12.010

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02 A. Dittrich et al. / Ecologica

t al., 2015; Tomscha and Gergel, 2016). However, the total num-er of such studies remains small (Plieninger et al., 2016) andhe published research are either based on public statistics andnfrastructure indicators (Renard et al., 2015), land cover maps andistorical aerial photography (Lautenbach et al., 2010; Tomscha andergel, 2016) or literature and technical reports (Morán-Ordóñezt al., 2013). Despite the variety of methods applied, they com-only conclude that (i) better baseline information on the past

rovisioning of ES is needed and that (ii) ignoring time will limithe understanding of complex ES dynamics and interactions (e.g.enard et al., 2015; Tomscha and Gergel, 2016).

The use of historical data may provide insights into the diverseets of ES that have shaped and maintained agricultural cul-ural landscapes (Antrop, 2005), which are the outcome of theo-evolution between human society and the environment overime (UNESCO, 2014). Such cultural landscapes are the result ofuman-induced changes through traditional land-use systems (i.e.,ractices that are not part of modern, intensive agriculture; Bignalt al., 1995) intended to fulfil societal demands for agro-ecologicalecosystem) products and services (Antrop, 2005; Fisher et al.,009). Traditional cultural landscapes, found across the globe, con-ribute to aesthetic qualities (Hartel et al., 2014) and foster genetic,rganismal and ecological diversity (e.g. Heath and Tucker, 1995;erzog, 1998). Furthermore, such landscapes preserve regionalgricultural knowledge and the diversification of management sys-ems which provide a buffer against unforeseen stochastic eventsr disturbances, thereby increasing landscape resilience (Barthelt al., 2013).

Multifunctional cultural landscapes are valued for their ecolog-cal, social and historic functions (Barthel et al., 2013; Plieningert al., 2013), yet they are vulnerable to the twin threats of agricul-ural intensification and abandonment due to their low economiceturns and changing perceptions of their value (Hanspach et al.,014). Both abandonment and intensification have led to a lossf numerous provisioning ES and the related agro-biodiversityorldwide (von Wehrden et al., 2014) and fundamentally altered

raditional cultural landscapes. In order to establish strategieso maintain and protect cultural landscapes and their relatedgro-biodiversity, a better understanding on how such landscapeseveloped as a result of their environmental conditions and anthro-ogenic use is needed (Farina, 2000; Morán-Ordóñez et al., 2013).

Compiling data on historical provisioning of ES in cultural land-capes as a starting point for detailed (temporal) analyses poseshallenges, mainly due to the fact that historical data cannot beirectly collected but has to be derived from existing data sources.

n particular, spatially explicit data on the provisioning of ES areard to come by. Historical aerial photography is probably mostromising (e.g. Lautenbach et al., 2010; Tomscha and Gergel, 2016)

n this context but also restricted by data availability when workingcross broad spatial scales or over long periods of time. Therefore,here is a need to find proxy indicators that can capture historicS provision and to relate those services to cultural land forms andnvironmental conditions.

In this study, we apply the ES concept to a dataset on the dis-ribution of historical farmhouses, construction materials used,illage and farm types throughout Germany in 1950 and beforeEllenberg, 1990). We extracted information on ES products, whichre defined as the goods and benefits derived from ES (Haines-oung and Potschin, 2013). While we identify and investigate somef the interdependencies of ecological and human systems thathape cultural landscapes, our study has two main aims: first,e analyse whether different traditional land-use systems can be

elated to differences in regional ES richness; second, we exploref the spatial distribution of service diversity can be explained by

cators 75 (2017) 101–110

environmental conditions such as precipitation, temperature andterrain ruggedness.

2. Data and methods

2.1. Study area

Germany is characterized by large topo-climatic gradients (alti-tude: −3–2962 m a.s.l., mean annual precipitation: 483–2340 mm,mean annual temperature: −3.7 – 11 ◦C, mean annual sunshineduration: 1376–1873 h) which can be related to the various formsof cultural landscapes and rural construction methods found(Ellenberg, 1990). Traditional land-use systems in Germany untilapprox. 1800 mainly aimed at the continuous supply of multipleproducts, rather than on optimizing yield (Beck, 1986). While landclose to villages was mainly used for crop production, grasslandsand more distant forests were used for livestock keeping (Schulze-Hagen, 2004). While the change from natural to rural landscapeswas gradual, two major periods of land-use change have beendescribed (e.g. Antrop, 2005; Haase et al., 2007). Within the firstperiod (19th century until the Second World War, Fig. A1), com-mon land was increasingly privatized and used for crop production,chemical fertilizers were first introduced, mechanization of agri-cultural production began and livestock housing systems becamemore popular. The second period of land-use change (post-WorldWar landscapes) can be characterized by (1) the intensive use ofchemical fertilizers and plant protection products (Spielman andPandya-Lorch, 2009), (2) land consolidation (Bičıı́k et al., 2001),the exchange of small and scattered agricultural areas betweendifferent farmers in order to form larger, continuous fields witha single owner (FAO, 2015), (3) further mechanisation and spe-cialisation of agricultural systems and (4) industrial livestockkeeping with intensive grassland management (Schulze-Hagen,2004). Overall, competitive advantages due to environmental con-ditions, economies of scale in production and the use of externalinputs to bolster production led to increased land-use specializa-tion and landscape homogenization (Blaxter and Robertson, 1995).Both periods of change have fundamentally transformed culturallandscapes in Germany and led to a degradation of many ES and asevere loss of biodiversity (e.g. Poschlod et al., 2005).

2.2. Overview of data sources and methods applied

Within the study at hand, we used different data sources andmethods to answer our two main questions (Fig. 1). Further detailson each of the steps are described in the following (see sectionMaps and Regional descriptions for more information about thedata recorded by Ellenberg; see section Spatial regression of historicecosystem service diversity and environmental variables for environ-mental variables analysed).

2.2.1. Historical information about rural landscapes in GermanyHeinz Ellenberg (1913–1997) was a German botanist, who

mainly conducted research in the field of vegetation ecology anddeveloped a 9-point scale in order to rate the preferences of plantsfor environmental factors (Leuschner, 1997). Beside this work, hewas also interested in the temporal evolution of housing types intraditional cultural landscapes. This interest resulted in the publi-cation of his book “Bauernhaus und Landschaft” (“Farmhouse andLandscape”, 1990). By compiling maps, notes and photographs,Ellenberg collected this rich and unique source of data about his-torical building types, construction materials, farm types, village

forms, and landscape elements and their spatial distribution inGermany. He gathered data between 1932 and 1988 and explic-itly stated that his aim was to provide historical information aboutthe rural landscapes in 1950 and before throughout Germany. We

A. Dittrich et al. / Ecological Indicators 75 (2017) 101–110 103

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Fig. 1. Schematic overview of the data used to address the two aims of the

sed this collection of data as source of information for historicalS products used by the rural population at this time (see below).

.2.1.1. Maps.he central element of Ellenberg (1990) are 80 maps (Fig. 2), basedn either the German topographical map (spatial resolution ofpprox. 10 × 12 km) or the UTM (Universal Transverse Mercator)oordinate system (10 × 10 km). All observations per grid cell wereecorded by Ellenberg on an ordinal scale with five categories: veryften, often, medium abundant, rare, and very rare (see Fig. 2).ll grid cells which were not surveyed were labelled as ‘missing

nformation’ and were hence excluded from our analysis.

.2.1.2. Regional descriptions.llenberg used the spatial information gathered in his maps forelineating homogenous landscape zones, which differ from eachther regarding their building types, material usage and typef villages. In doing so, he differentiated nine regions, 45 sub-egions (the main reference unit of Ellenberg’s landscape zones)nd 97 sub-areas (with distinct differences in minor importantharacteristics). To further characterise these regions and to com-lement the information from the maps, he used notes taken inhe field and information from the literature that resulted in veryetailed regional descriptions including information on e.g., land-cape scenery, agricultural management practices, climate and soilonditions.

.2.2. Identification of indicators for historical ecosystem serviceroducts

We carefully reviewed both maps and regional descriptionso identify indicators for historical ES. All ES products identifiedere independently verified by two of the authors. Identification of

ervice products (we applied the CICES v4.3 classification, Haines-oung and Potschin (2013)) was often directly possible, e.g. foredges used to separate livestock, trees planted around houses asind protection, orchard meadows, organic material used in walls

o apply clay (see Fig. 3), and stable wood in half-timbered con-tructions (see Fig. 3). In addition, some descriptions and mappednformation were indirectly linked to certain service products. Asn example, the farm house types (see Fig. 2) were used as proxyor the importance of either crop production or livestock keepingystem or the occurrence of houses with certain roof pitches was

sed as an indicator for the use of straw and reed as roof coveringaterial (see Fig. 3). In total, we found indication for eight ES classes

overing 32 ES products. A detailed overview about the identifiedervice products is given in Table 1.

, including the applied methods. CAR = Conditional Autoregression Model.

2.2.3. Distribution of traditional land-use systems and ecosystemservices richness

To get the most complete picture of ES products provided,we compiled a complete list of all ES products for each of the45 sub-regions based on the regional descriptions (which sum-marized information from the maps but also provided additionalinformation as some ES were not represented in the maps, seeTable 1 and section Regional descriptions). As opposed to the maps,regional descriptions provided only binary information on pres-ence/absence of ES products. We hence determined the richness ofES products at the sub-region level and used flower plots (R pack-age: graphics, R Core Team, 2013) to illustrate spatial differences inES richness in relation to the dominant traditional land-use systemby means of visual comparison (Fig. 5).

To identify the dominant traditional land-use system for eachof the 45 sub-regions, we analysed those maps providing detaileddata on the relative importance of livestock keeping, crop produc-tion or mixed land-use systems (as defined in Baldock et al., 1994).In order to finally differentiate between the three main land-usesystems, we applied the following rule: if farm houses and villageforms typical for livestock keeping (see Fig. 2) were on averagemore abundant by at least one ordinal scale category as comparedto those typical for the production of cereal crops, wine or fruits,the respective sub-region was assigned to the group of livestockkeeping systems (analogously for crop production systems). If thedifference in abundance was less than one category, sub-regionswere assigned to mixed systems (Fig. 5).

2.2.4. Spatial regression of historic ecosystem service diversityand environmental variables

For the spatial regression, we had to restrict our analysis to the15 ES products represented in Ellenberg’s grid-based maps (seeFig. 1, marked ‘M’ in Table 1) to extract spatial explicit accountsof those ES products. By taking into account the relative abun-dance measurements (ordinal scale from one to five) recorded forevery grid cell, we calculated the Shannon diversity (Shannon andWeaver, 1949) of ES products per cell. To ensure spatial compat-ibility between the two types of maps used by Ellenberg (1990),we resampled the data based on UTM raster maps to match thegrid cells of the topographical maps of Germany. For analysingthe relationship between these 15 ES products and environmen-tal gradients, we selected soil quality (Roßberg et al., 2007), mean

precipitation and temperature during the vegetation period (DWD,2014), slope, altitude as well as terrain ruggedness (BKG, 2014) aspotentially meaningful environmental predictors. The arithmeticmean of each environmental variable was calculated per grid cell

104 A. Dittrich et al. / Ecological Indicators 75 (2017) 101–110

Table 1Indicators for historical ecosystem service products and descriptions extracted from Ellenberg (1990). Classes, divisions and groups refer to the CICES classification (version4.3; Haines-Young and Potschin, 2013). Data source refers to either spatial data based on maps (M) or data based on regional descriptions (R), shown in the last column. Asmore detailed information about the different types of fodder were provided in the regional descriptions than recorded spatially explicit ‘Ma’ indicates the map combiningall types of fodder.

Class [Division](Group)

Short name Service product Description Data source

ProvisioningBiomass [Nutrition](Cultivated crops)

Cultivated crops Cereal crops E.g. wheat and rye M, RVegetables E.g. potatoes, pumpkins and vegetables in

the gardenR

Orchards Fruit production on a big scale M, ROrchardmeadows

Extensive fruit production M, R

Wine Vineyards RHop Hop production M, R

Biomass [Nutrition] (Rearedanimals and their outputs)

Reared animals Honey Bee keeping RWild animals Fish Inland fishery R

Biomass [Materials](Fibres and othermaterials from plants,algae and animals fordirect use or processing)

Materials for direct use Carrier material Material in wall used as base to apply clay(e.g. straw, bendable wood)

M, R

Insulation Plant material applied between straightwood (e.g. moss)

R

Ropes Hemp was used to produce ropes RStable wood Stable wood that was used for

half-timbered constructions (mainly oak,partly spruce)

M, R

Straight wood Straight wood that was used forblockhouses (mainly spruce)

M, R

Timber Timber used for building without specificpurpose

R

Weatherprotection

Weatherproof wood used to protect walls(boards, planks and window shutters)

M, R

Weatherprotectionroofing

Plant material used to cover roofs (straw,reed)

M, R

Aestheticappreciation

Flowers used to decorate house R

Ornamental use Special wood used for handcrafts R

Biomass [Materials](Materials from plants,algae and animals foragricultural use)

Materials for agriculturaluse

Mulching Plant material was used as mulch forlivestock bedding and put back to arableland

R

Peat Peat was added to soil to increaseproductivity

M, R

Plaggen Calluna vulgaris was extracted fromcommon land and mixed with animalfaeces

M, R

River sediments Regular floods increased soil productivityin the floodplains

Fodder-hay Hay as fodder for livestock (mainly inlivestock housing systems)

Ma, R

Fodder-oak Acorns were used for pig fattening (mainlyin pig-housing systems)

Ma, R

Grazing Livestock was fed with grass (mainly kepton pastures)

Ma, R

Grazing/fodder-hay

Livestock was fed with grass (kept onpastures) as well as hay production asfodder (livestock housing systems)

Ma, R

Hedges Hedges used to separate allotments forlivestock keeping

M, R

Biomass-based energysources [Energy](Plant-based resources)

Plant-based energy Firewood-hedges

Hedges were cut down or prunedregularly, material used as fuel

M, R

Fire wood-trees Parts of trees extracted regularly and usedas fuel

R

Fuel-peat Peat was extracted and used locally as wellas transported to bigger cities

M, R

Regulation/MaintenanceGaseous/air flows[Mediation of flows](Storm protection)

Storm protection Wind protection Trees and hedges around houses planted aswind protection

R

No equivalent CICESclass

Fire protection Fire protection Big trees near houses attracted lightningsand hence protect nearby houses

R

A. Dittrich et al. / Ecological Indicators 75 (2017) 101–110 105

Fig. 2. Exemplary maps compiled by Ellenberg. Left: UTM raster map with information on the frequency of occurrence of separate barns typical for regions with highimportance of crop production. Right: German topographical map with abundance information on certain house forms (so-called “Einfirst-, Winkel-, and T-Höfe”) which aretypical farm houses in regions with high importance of livestock keeping. We added the red box to highlight the ordinal scale with five categories: biggest dot = very oftenand smallest dot = very rare. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Source of the images: Ellenberg (1990).

Fig. 3. Examples of building materials identified as ecosystem service products. Left: use of reed as roof cover was defined as “Weather protection roofing” and timber usedfor frame construction of half-timbered construction was classified as “Stable wood”. Right: different organic materials were used in walls as base to apply clay, in this casebendable wood and wooden poles are shown, these materials were classified as “Carrier material”.(For interpretation of the references to colour in this figure legend, thereader is referred to the web version of this article.)

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ource of images: Ellenberg (1990), right panel modified after Bedal (1985).

f the topographical map of Germany (R package: raster; Hijmanst al., 2014). Prior to analysis, all variables were tested for collinear-ty and due to their strong correlation with precipitation (|r| > 0.7;ormann et al., 2013) the variables slope, ruggedness and altitudeere identified as redundant (see Fig. A2). However, we retained

uggedness as predictor as it was not highly correlated with vari-bles other than precipitation and we assumed that at least one

easure of topography may be important for our analysis. Fur-

hermore, ruggedness integrates information captured by slope andlevation change over a defined area (in our case: 1000 m2) and isherefore a proxy for the accessibility of a landscape.

The selected variables (soil quality, mean precipitation, temper-ature and ruggedness) were then standardized in order to allowbetter comparability of the model outputs (Schielzeth, 2010). Totest Shannon diversity of ES against the standardized environ-mental predictors while accounting for spatial autocorrelation, weapplied a spatial conditional autoregression model (CAR, R pack-age: spdep; Bivand et al., 2014), with a neighbourhood size of four

(“Rooks case”). This type of model is appropriate for identifyingdrivers on spatial patterns of ES (Mouchet et al., 2014). The residualsof the model were tested for remaining spatial autocorrelation.

1 l Indicators 75 (2017) 101–110

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Table 2Relationship between Shannon diversity of historical ecosystem services and envi-ronmental variables (z-transformed) and their interactions at map sheet scale(∼130 km2) based on results of the spatial conditional autoregression model (CAR).

Coefficients Estimate Std. Error p-value

Soil Quality 0.026 0.017 0.135Ruggedness −0.002 0.030 0.952Mean Temperature during Vegetation Period 0.017 0.025 0.482Mean Precipitation during Vegetation Period 0.083 0.039 0.032Soil Quality: Ruggedness 0.041 0.026 0.114Soil Quality: Mean Temperature 0.003 0.016 0.841Soil Quality: Mean Precipitation −0.013 0.027 0.633Ruggedness: Mean Temperature −0.028 0.014 0.045

06 A. Dittrich et al. / Ecologica

. Results

.1. Regional richness of historical ecosystem service products

The review of ES products assessed within the scope of this studyevealed that the northern sub-regions III-1, I-1 and II-3 as wells the central and southern regions V-6, VI-1, VI-3 and VIII-2 hadhe highest richness ranging from seven to 14 products (Fig. 4a).

ith eight ES products, sub-region IV-5 had the highest richnessn the eastern part of Germany. In the proximity of northern andouthern sub-regions characterised by high numbers of ES prod-cts, areas are located with similar average numbers of productsfive to six); the same is true for sub-regions in the central partf Germany. The lowest number of ES products was identifiedor the eastern sub-regions IV-1 and IV-2, most parts of regionX and sub-region III-8 in the south as well as the western sub-egions V-9 and III-5 with a maximum of three different ES pro-ucts.

“Materials for direct use”, “Materials for agricultural use” andCultivated crops” were mentioned in the regional descriptions forearly all regions. However, despite the comparatively high num-er of ES products belonging to the class “Materials for direct use”10 in total, see Table 1), this class contributed more than 50% ofhe ES products assessed in only half of all sub-regions, indicatingarge spatial differences in the use of the related products (Fig. A3).ompared to other areas, sub-region VIII-2 was characterized by aigh richness of ES products which were used as building materi-ls and belonged to the service class “Materials for direct use” (seeower plots in Fig. 5 and Table A1). The second highest number ofuch ES products (four) was recorded in the sub-regions V-6, VI-1,I-2, VI-4 and VII-3 (Fig. 4a). Sub-regions with two to three ES prod-cts belonging to the class “Cultivated crops” were mainly located

n region III and V. ES products belonging to the classes “Rearednimals”, “Wild animals”, “Storm protection” and “Fire protection”ppeared to be spatially restricted as they were referenced onlyn 16% of all sub-regions. For example, the ES product assigned toStorm protection” could only be identified in the northern regions

and II as well as in VII-2 in the west of Germany (see Fig. 5 andable A1).

.2. Distribution of traditional land-use systems

Livestock keeping was dominant in 45% of sub-regions and mostrominent Northern and Southern Germany whereas crop pro-uction was dominant in 21% of sub-regions that were mostlyistributed in Eastern and Central Germany. The remaining 34%f sub-regions that were classified as mixed systems were mainlyound in Central Germany (see Fig. 5). The most diverse livestockeeping system was found in sub-region III-1 with four ES prod-cts referring to different types of fodder (see Table A1) within thelass “Materials for Agricultural Use”. In this sub-region, grazing-orests for pigs (indicated by “fodder-oak”, see Table 1), livestockousing systems (indicated by “fodder-hay”) as well as outdooreeping systems (indicated by “grazing”) were utilized. More thanne fodder-related service product was also recorded for II-3, V-6nd VI-3.

Sub-regions with either high (e.g. I-1, III-1, II-1, VII-2, V-6, and II-) or low (e.g. III-8, IV-2, IX-3, IX-4, and IX-5) numbers of ES classesnd products mostly co-occurred with livestock keeping or mixedystems. The only exceptions were IV-5, V-3 and V-7, for which

e found high numbers of ES products in crop production systems.verall, sub-regions with average to lower numbers of ES products

ended to be crop production systems, e.g. IV-1, IV-3, V-2, and V-9see Fig. 5).

Ruggedness: Mean Precipitation −0.027 0.012 0.030Mean Temperature: Mean Precipitation 0.025 0.017 0.127

3.3. Relationship between diversity of historical ecosystemservice products and environmental conditions

The CAR model was highly significant (p < 0.001) with a R2 of0.410. In general, precipitation had the strongest positive influenceon the service diversity (Fig. 4b, Table 2). Also, the interactionsbetween ruggedness and temperature as well as ruggedness andprecipitation were significant (p < 0.05). This means that in areaswith flat terrain but with high temperatures or high precipitation,the number of services was greater compared to other regions.The extent of spatial autocorrelation further suggests that otherregion-specific drivers not specifically considered as predictors inthe model were affecting the diversity of provisioning service prod-ucts.

4. Discussion

This study provides the first spatially explicit account of theprovision of ES products historically used at a national scale inGermany. By basing our analyses on regional descriptions and mapsof farmsteads, village forms, agricultural practices and specificlandscape features, we were able to provide further insights on thecharacteristics and historical configuration of cultural landscapes.We found that a high richness of these products was associatedwith distinct types of traditional land-use systems (in particularlivestock keeping and mixed farming systems). Furthermore, spa-tial differences in service diversity could be partially explainedby environmental gradients. The present study contributes to therestricted numbers of studies that investigated temporal aspects ofES at local/regional scales (Haase et al., 2007; Morán-Ordóñez et al.,2013; Palomo et al., 2014; Renard et al., 2015; Tomscha and Gergel,2016).

4.1. Historical distribution of ecosystem service products

In the past, when traditional land-use systems were more abun-dant in Germany, large regional differences in the number of serviceproducts existed. Based on both the regional descriptions andthe maps, we found a hot spot of ES products in North-westernGermany (Fig. 4), where livestock keeping and mixed farming sys-tems dominated agricultural production (Fig. 5). These systemsrelied, for example, on shrubs as fences and sources of fuel, as wellas acorns for pig fattening. Since soil fertility in these areas wasgenerally low, heather was used as bedding and converted into fer-tilizer over winter time. Further, extracted peat served as fertilizer.While these different land-use types certainly represent more mul-

tifunctional landscapes, the higher numbers of ES in North-westernGermany may also partly have arisen from delayed agriculturalintensification up until 1950 (Büssis, 2006) and an overall slowerpopulation growth (Ehmer, 2004).

A. Dittrich et al. / Ecological Indicators 75 (2017) 101–110 107

Fig. 4. Historical distribution of ecosystem service products identified based on data provided by Ellenberg (1990) and classified according to CICES v4.3 (Haines-Younga n. Serp 2). Spo rsion

iiadssEtfnmcw

ect(apdhea

nd Potschin, 2013). Panel a: Richness in ecosystem service products per sub-regiohotographs). Panel b: Diversity in ecosystem service products per grid cell (∼130 kmf the references to colour in this figure legend, the reader is referred to the web ve

The spatial model helped us explain the lower numbers of ESn the central part of Germany, as precipitation during the grow-ng season had an overall positive influence on ES numbers. Drierreas can be found in the eastern part of this region, which wasominated by crop production − also due to its extremely highoil fertility. Here, most of the building types relied on bricks andtones and not on organic material, which led to a lower number ofS implicitly recorded by Ellenberg. Precipitation also determineshe range of oak species (Ellenberg and Leuschner, 2010) neededor half-timbered constructions, which resulted in slightly higherumbers of ES in the western part of this region, characterized byore rainfall. In contrast to our assumption that in areas with a

omplex topography the number of ES would be higher, terrainas not a good predictor for the distribution of ES.

To improve the spatial model, further predictors such as socio-conomic data that capture human and cultural developmentsould be included. Specifically, data on population density, migra-ion and growth, income or the distribution of ethnic minoritiese.g. Sorbs, Frisians) could prove particularly valuable in futurenalyses, e.g. such as done by Dunford et al. (2015). This is sup-orted by Hartel et al. (2014) who showed that poverty is the mainriver for the existence of traditional land-use systems that featureigh ES diversity in Romania. However, to our knowledge, spatially

xplicit data on socio-economic variables between the 19th centurynd the Second World War in Germany are not available.

vices (in total 29) were identified from regional descriptions (including notes andatial accounts of services (in total 15) were extracted from maps. (For interpretationof this article.)

4.2. Challenges of mapping historical ecosystem service indicators

Spatially explicit data are needed to establish causal relation-ships between historical ES provisioning and local environmentaland socio-economic variables (Mouchet et al., 2014). Aerial pho-tography, which assists topographic mapping since the 1920s, isobviously a promising approach and widely used in historical land-scape analysis (e.g. Dittrich et al., 2011). However, data availabilityat larger scales is limited and data processing is time consuming(e.g. geographical rectification, digitizing by hand). Other promis-ing approaches are intensive literature reviews as conducted byMorán-Ordóñez et al. (2013) which can provide detailed informa-tion on how differently cultural landscape sustained the livelihoodof their rural populations.

Even though this study assessed a total of 32 service productshistorically used in Germany, this set is far from being comprehen-sive. While provisioning services could be considered extensively,cultural, regulation and maintenance services were only scarcelyaccounted for. Due to this inherent bias in the data source, wewere not able to establish a causal relationship between the typeof traditional land-use system and the number of ES. To achievea more complete account of ES, other data sources would have tobe considered. For example, to identify regulating services such as

flood mitigation, historical land cover data could be analysed (e.g.Früh-Müller et al., 2014).

108 A. Dittrich et al. / Ecological Indicators 75 (2017) 101–110

Fig. 5. Spatial distribution patterns of ecosystem service products (grouped by ecosystem service classes after CICES; see Table 1 for further details) for each of the sub-regionsdefined by Ellenberg (1990). Petal length in the flower plots indicates differences in service richness per service class among sub-regions. Colour codes for the flower plotsrefer to the main service classes. Grey bars show abundance of livestock keeping and crop production systems.

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.3. Relevance for today’s ecosystem management andnvironmental policies

Despite the limitations noted above, the data source and anal-sis presented here provided insights in the provisioning of ES inistorical landscapes partly not existent anymore due to intensive

and-use changes since then. This baseline of ES provisioning aidsur understanding of the development of current land-use patterns,s for example North-western Germany is still known for inten-ive livestock production whereas crop production is importantor Eastern Germany (Dittrich et al. unpublished). Furthermore, byomparing past and present utilization of services a better under-tanding regarding the processes leading to the generation andxtraction of ES can be gained (Morán-Ordóñez et al., 2013; Renardt al., 2015) making the loss of certain ES traceable.

In that respect, understanding the historical co-evolution of cul-ural landscapes and ecological resource use (here conceptualizedn terms of ES) may be important to preserve or regain multi-unctional landscapes. If we wish to maintain the diversity andnique character of cultural landscapes in the face of increasingemporally and spatially homogeneous agricultural land-use prac-ices then there is a need for these cultural landscapes to replicatehe multi-faceted and multifunctional uses from which such land-capes arose. Therefore, it would be highly relevant to compareistorical and legally-proposed land-use options for their impactn the multifunctionality of landscapes. A large number of subsidychemes (e.g. Thomas et al., 2009; Schleyer and Plieninger 2011;lrich and Riecken, 2012) for historical land-use options equally

how that knowledge on their impact on ES is necessary to identifyore effective and regionally adapted options.A better understanding of the ES such landscape provided and

onsideration of how these unique landscapes could provide sim-lar suites of services suitable for the 21st century would help toestore cultural landscapes. Their values come not only from theirultural importance, but also form the flows of services they pro-ide. However, most of today’s subsidies and ‘greening’ measuresCommon Agricultural Policy; BMEL, 2015) aim at protecting bio-iversity aspects (e.g. hedges planted to separate livestock in theast are important for biodiversity today; Ponti et al., 2005) thato-evolved with cultural landscapes. As societal demands on land-capes are changing continuously (e.g. Haase et al., 2007) it is veryikely that “new” regulating/maintenance as well as cultural ser-ices are wanted by society, which in turn may create trade-offsith the conservation targets of the present subsidies. Balancing

hese trade-offs would require workshops with all stakeholdersnvolved (Förster et al., 2015; Seppelt and Cumming, 2016).

. Conclusion and outlook

The countryside in Europe is becoming less and less a placehat provides a livelihood for the majority of the inhabitants,hereby “landscape and rural life are becoming ominously dis-oined” (Lowenthal, 1997). The presented study for historical ESrovisioning helps to understand how landscapes have been usednd developed in the past in Germany, a task for which the ESoncept proved to be a useful lens through which to assess the ben-fits for society provided by cultural landscapes. While the presenttudy focuses on Germany, it may be regarded as representativeor many regions in Europe. We further suggest that the investi-ation of historical ES could be a valuable approach to clarify thedentity and functionality of cultural landscapes in the changing

patial context of society (Antrop, 2005). As the existence of culturalandscapes depends on their active use (e.g. Morán-Ordóñez et al.,013), assessing historical ES may also provide incentives for theaintenance of such landscapes. This is partly realized by current

ators 75 (2017) 101–110 109

subsidy programs, which aim at protecting former ES in culturallandscapes to foster the conservation of agro-biodiversity and land-scape aesthetics. However, more sustainable options to reconnectpeople to the rural landscape should also be explored. To supportacceptance and application of these incentives we advocate to alsoappeal to the history and former diversity of ES that shaped thehomeland of today’s residents.

Acknowledgements

The presented study is the outcome of a Ph.D. synthesis work-shop of the Helmholtz Research School for Ecosystem Servicesunder Changing Land Use and Climate (ESCALATE) at the HelmholtzCentre for Environmental Research – UFZ and the Leuphana Uni-versity Lunenburg. The work was funded partly by the HelmholtzProgramme ‘Terrestrial Environment’. We thank Ludwig Ellenbergwho approved the usage of the data gathered by his father HeinzEllenberg. We further thank Wulf Jung from GISCON for provid-ing digital versions of the maps published in the book as well asDietmar Roßberg for providing a Germany-wide soil quality map.

Appendix A. Supplementary data

Supplementary data associated with this article can be found,in the online version, at http://dx.doi.org/10.1016/j.ecolind.2016.12.010.

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