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Habitat indicators for evaluating European farmland biodiversity
F. Herzog1*, G. Lüscher1, M. Arndorfer2, M. Bogers3, K. Balázs4, A. Báldi5, R.G.H. Bunce3, 6, P. Dennis7, E. Falusi4, J.K. Friedel2, I.R. Geijzendorffer3, 8, T. Gomiero9, 10, P. Jeanneret1, G. Moreno11, M.-L. Oschatz2, M.G. Paoletti9, J.-P. Sarthou12, S. Stoyanova13, E. Szerencsits1, S. Wolfrum14, W. Fjellstad15, D. Bailey1
*Corresponding author: [email protected]
1 Agroscope, Reckenholzstrasse 191, Zurich 8046, Switzerland.2 University of Natural Resources & Life Sciences, Gregor Mendel Strasse 33, Vienna 1180,
Austria.3 Alterra, Wageningen UR, Droevedaalse steeg 3, Wageningen 6708 PB, The Netherlands.4 Institute of Environmental & Landscape Management, Szent Istvan University, Páter
Károly u. 1, Gödöllö 2100, Hungary.5 MTA Centre for Ecological Research, Alkotmány u. 2-4, Vácrátót 2163, Hungary6 Estonian University of Life Sciences, Kreuzwaldi 5, Tartu 51041, Estonia 7 Institute of Biological, Environmental and Rural Sciences, Aberystwyth University,
Penglais Campus, Aberystwyth SY23 3DD, United Kingdom.8 Tour du Valat, Institut de recherche pour la conservation des zones humides méditerra-
néennes, Le Sambuc, Arles 13200, France.9 Department of Biology, Padova University, via U. Bassi 58/b, Padova 35121, Italy.
10 Institute of Environmental Science and Technology (ETSE/ICTA), Universitat Autonoma
Barcelona, Campus of Bellaterra, Cerdanyola del Valles 08193, Spain.
11 Forestry School, University of Extremadura, Av. Virgen del Puerto 2, Plasencia 10600, Spain.
12 UMR 1201 Dynafor, INRA, Chemin de Borde-Rouge, Castanet-Tolosan 31326, France.13 Institute of Plant Genetic Resources K. Malkov, Sadovo 4122, Bulgaria. 14 Centre of Life and Food Science, Technical University of Munich, Alte Akademie 12,
Freising 85354, Germany.15 Norwegian Institute of Bioeconomy Research, PO Box 115, Ås 1431, Norway.
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Abstract
Habitat indicators are cost effective biodiversity indicators demanded by stakeholders and re-
quired for regional and global biodiversity monitoring. We mapped 195 farms of different
types in twelve case study regions across Europe and tested 18 habitat indicators for scientific
validity, information content and interpretability. We propose a core set consisting of (i) four
indicators to describe farm composition and configuration (Habitat Richness, Habitat Di-
versity, Average Size of Habitat Patches, Length of Linear Elements), (ii) three indicators ad-
dressing specific habitat types (Crop Richness, Shrub Habitats, Tree Habitats) and (iii) one in-
terpreted indicator (Semi-Natural Habitats). As a set, the indicators allow to evaluate the hab-
itat status of a farm and to track changes occurring due to modified land use and/or manage-
ment, including agri-environmental measures. The farm habitat maps can provide ground
truth information for regional and global biodiversity monitoring.
Key words: Agricultural biodiversity, agri-environment scheme, ecological focus area, essen-
tial biodiversity variables, landscape diversity, monitoring, policy evaluation
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Graphical Abstract
Highlights
o We propose a core set of eight farm scale habitat indicators
o The indicators were tested on 195 farms across Europe
o Habitat indicators can enable policymakers to evaluate agri-environmental measures
o Habitat maps provide a bases for species sampling and for ground truthing of remotely
sensed information
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1. Introduction
Biodiversity change is one of the biggest challenges of human society (Perrings, 2014). It is
all the more important that the status and evolution of biodiversity be known and monitored.
Due to its complexity, biodiversity cannot easily be measured and appropriate indicators or
descriptors or surrogates need to be selected. This selection will always require finding a bal-
ance between trade-offs, e.g. accuracy vs. generality, cost-effectiveness vs. certainty, etc.
(Lindenmayer et al., 2015, Herzog et al., 2016). Essential Biodiversity Variables (EBV) have
recently been proposed (Pereira, 2013) and ecosystem structure is one of the six EBV classes.
Whilst ecosystem diversity is one of the three components of biodiversity (genetic diversity,
species diversity, ecosystem diversity; CBD, 1992), the occurrence, diversity and amount of
habitat are also important determinants of species diversity (Harrison and Bruna, 1999;
Fahrig, 2001; Billeter et al., 2008; Liira et al., 2008; Fahrig, 2013).
There is a plethora of habitat or landscape indicators (McGarigal et al., 2002; Dramstad,
2009) and indicators need to be selected to provide non-redundant information for a specific
context, scale and environment (Bailey et al., 2007ab; Fahrig et al., 2011; Schindler et al.,
2013). Here we focus on agricultural landscapes, where agro-biodiversity conservation usu-
ally operates via habitat restoration and conservation. Through agri-environment schemes,
farmers are compensated with payments to modify land use and farming practice to provide
environmental benefits (Kleijn & Sutherland, 2003; Stoate et al., 2009) and in the European
Union, five per cent or more of each farm need to be managed as ecological focus area under
the cross-complicance regulation (EU, 2013). Not enough is known about the effectiveness of
these schemes (e.g. Blomqvist, Tamisb & de Snoo, 2009; Gabriel et al., 2010; Concepcion et
al., 2012) as very few ‘pockets of good monitoring practice’ for agri-environment schemes
exist (The European Court of Auditors; ECA, 2011).
In agricultural landscapes, the key stakeholder is actually the farmer. He or she decides on
management practices, including the implementation of ecological focus areas and the adop-
tion of an agri-environment scheme. This makes the farmer the most important decision-
maker on conservation issues for farmland biodiversity (Weibull et al., 2003; Öberg et al.,
2007, van Haaren et al., 2012). There is thus a need for habitat indicators at the farm scale,
which capture the status of farmland biodiversity and can inform about the implementation
and effectiveness of agri-environmental policies. Ideally, farmland biodiversity monitoring
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would provide a building block also for large scale biodiversity monitoring schemes such as
the one constructed around EBV. Yet, monitoring habitat diversity at the farm scale is chal-
lenging. Farms are legal/economic units that are rented/owned by the famer and consist of
various farmed (e.g. arable fields) and unfarmed habitats (e.g. hedgerows). These may be in-
termingled with plots of land owned by other farmers or with land that has no agricultural
function. The non-contiguous nature of many holdings means that the spatial arrangement of
the habitats of an individual, non-consolidated farm has no ecological meaning.
The objective of this study is to propose a core set habitat diversity indicators at farm scale,
which are:
(i) scientifically sound and ecologically meaningful, i.e. methods are standardized
clearly described and can be reproduced, results can be interpreted;
(ii) attractive and useful for stakeholders, they differentiate between farms and yield
information which is useful for farmers, administrators, policy makers, represent-
atives of NGOs, etc.;
(iii) applicable across Europe, i.e. the methods and the resulting indicators can be ap-
plied to major farm types (arable, grassland, etc.) and across major bio-geograph-
ical regions.
To this end, we mapped the habitats of 195 farms across 12 European case study regions and
a variety of farm types. The next section describes the approach to farm habitat mapping, in-
dicator selection and calculation. By detailing the decisions which had to be made to allow for
a standardized mapping process, it goes beyond the usual Methods section of a scientific art-
icle. In the following section, results for different categories of habitat indicators are presen-
ted. This involves straight forward indicators, which are needed to describe the composition
of a farm. Because stakeholders express interest in indicators, which attempt to capture the
ecological value of farm habitats from the perspective of biodiversity conservation, we also
tested various interpreted indicators that also involve some degree of expert judgment.
The results presented here are part of the findings of a European research project on farmland
biodiversity indicators relating to genetic diversity of crops and husbandry animals, species
diversity (vascular plants, earthworms, wild bees, spiders) and habitat diversity and also in-
volving farm management indicators. This article focuses specifically on habitat diversity in-
dicators, whilst a comprehensive overview of the results is available from Herzog et al.,
(2012) and more specific findings from e.g. Kovács-Hostyánszki et al. (2011), Last et al.,
2014, Lüscher et al., 2014ab). The original data have been published by Lüscher et al. (2016).
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2. Methods and principles for measuring habitat diversity at
the farm scale
Prior to the definition of habitat recording methods, an exhaustive literature review of poten-
tial farm-scale habitat indicators was conducted. Fifty-eight habitat indicators (data not
shown) were listed that were sensitive to change, reproducible, comparable across different
farm types, applicable at the plot- and farm-scale and possibly related to ecological function
and quality (Dennis et al., 2009). Many more habitat indicators exist (see e.g. McGarigal et
al., 2002) but were not appropriate for the farm scale, e.g. fragmentation or connectivity
measures due to the non-contiguous characteristic of farms. The list was then submitted to a
stakeholder advisory board, consisting of 20 professionals of national and European adminis-
trations and of non-governmental organisations (farmers associations, nature protection or-
ganisations, consumer and marketing organisations). The stakeholders assessed the indicators
according to pre-defined selection criteria (Pointereau and Langevin, 2012). For example, it
was essential that the indicators were easy to develop, to record, to use, be comprehensive and
flexible, low cost and be appropriate for use by farmers, consumers and administration. They
should also enable the assessment of the farmer’s progress, management plans and agricul-
tural policies and be applicable to all farm types across Europe. The interaction with stake-
holders worked through a series of workshops, starting with the kick-off meeting of the pro-
ject. Scientists synthesized information on potential indicators on fact sheets, which were then
evaluated in a structured process by the stakeholders. Evaluations of both, scientists and
stakeholders, where confronted at the workshops and consensus was sought. This process
yielded 18 candidate indicators, most of them with several sub-indicators. Habitat mapping
methods were then defined, which allowed to record the information needed to calculate those
indicators in a consistent manner by the respective teams of the case study regions.
2.1 Case study regions and definition of farms as units of investigation
In order to identify habitat indicators suitable for a broad range of farming situations, farm
habitat diversity was investigated in 12 regions occurring in 11 European countries. Four ma-
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jor farm types were represented: Field crops and horticulture; Specialist livestock grazing,
Mixed crop and livestock, and Permanent crops. The individual case study regions were ho-
mogenous in terms of biogeography and farm production type. In each region 10-20 farms
were randomly sampled (Table 1) and where both organic and non-organic holdings existed,
both were selected. See Herzog et al. (2012) for a detailed description of the case studies.
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Table 1: Description of the 12 European case study regions. Source: Herzog et al. (2012).
Country Region Abbre-viation
Farm type No of farms
Average farm size(ha)
Average N-input(kg / ha)
Total No of habitat types (all farms)
Comments
Austria March-feld
AT Field crops and horticul-ture
16 68 92 26
Intensively man-aged arable farming on the Pannonian lowlands.
France Gascony FR Field crops and horticul-ture 16 70 79 35
Mosaic of intensive to extensive crop production.
Nether-lands
Gelder-land
NL Field crops and horticul-ture 14 20 174 28
Intensively man-aged dairy and hor-ticulture systems.
Bulgaria Rhodope Moun-tains
BG Specialist grazing live-stock
16 24 125 51
High Nature Value grasslands pastured with cattle and sheep.
Switzer-land
Ob-walden
CH Specialist grazing live-stock
19 10 188 26
Mountain grassland with cattle, some traditional fruit tree orchards.
Spain (Dehesa)
Ex-tremadura
ED Specialist grazing live-stock 10 483 58 34
Scattered oaks with native pasture (cattle, sheep, pigs).
Norway Hedmark NO Specialist grazing live-stock 12 16 160 24
Boreal mountain grasslands with sheep pastures.
Wales Wales GB Specialist grazing live-stock
20 142 166 46
Coastal mountain grasslands with sheep and cattle pastures.
Germany Munich DE Mixed crops and livestock
16 60 215 29
Mixed farming with arable crops and grassland for cattle and milk produc-tion.
Hungary Ho-mokhát-ság
HU Mixed crops and livestock
18 96 52 61
Low-input Puszta grassland, few crop fields.
Italy Veneto IT Permanent crops (vine)
18 23 23 13
Farms specialized in wine production, mostly vineyards.
Spain (Olive)
Ex-tremadura
EO Permanent crops (olives) 20 8 62 23
Traditional olive groves.
The focus at farm scale contrasts with most landscape ecology studies in that it has no pre-
defined spatial cohesion. Individual fields can be far apart and intertwined with other farms.
Farm habitat included the Utilized Agricultural Area (UAA; e.g. crop fields, sown and per-
manent grassland, intensively managed orchards and vineyards) and the less intensively man-
aged parts of the farm associated with ecological structure (e.g. hedgerows, extensively man-
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aged orchards, wildflower strips and grazed forest). As the boundary between farmed and un-
farmed land fluctuated in some regions, specific definitions were used to assure inclusion of
all habitats affected by farming activities. The following habitats were selected:
The UAA managed by the farmer, minimal mapping unit was 400 m2 (areal habitats,
5 m minimal width);
Hedgerows, lines of trees, shrubby, grassy and herbaceous strips, water margins and
stone walls managed by the farmer or directly affected by agricultural management
(linear habitats >30 m, width 0.5 – 5 m);
Grazed forest (even if not legally part of the UAA) used by the farmer;
Small woods (<800 m2);
Aquatic habitat (<800 m2).
The following habitat categories were excluded:
Common grazing pastures as they are utilised by multiple farmers and their contribu-
tion to a specific farm in terms of biodiversity cannot be specified.
Forest, shrubby habitat and aquatic habitat >800 m2 as they represent a different eco-
nomic function;
Farm buildings (inclusive greenhouses) and their gardens;
Nature protection areas which are no longer part of the UAA.
2.2 Mapping and classification of farm habitats
A standard habitat mapping procedure originally developed by Bunce et al. (2008, 2011) was
adapted to deal with the assessment of farms (Dennis et al., 2012). The method applies a gen-
eric system a of habitat definitions, so-called General Habitat Categories (GHC) based on
plant life forms as described by Raunkiaer (1934). This enables universal application at the
European scale. A standardised set of field rules allows for the classification of habitats, and
habitat qualifiers provide additional information about the ecological and management attrib-
utes of each specific habitat patch.
Using this method each areal and linear element was delineated on a map, an aerial photo-
graph or a satellite image. GHC and habitat qualifiers were recorded for each mapped element
(see Appendix 1 for a complete list of habitat types which were recorded). The maps were di-
gitised in a geographical information system (GIS) according to a specific protocol (Dennis et 9
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al., 2012). The habitat type allocated was generally the same as the GHC, except for arable
crops and grasslands (see below and list of habitat types in the Appendix). Centralised valida-
tion was undertaken, maps were corrected if necessary and fed back to the local field work
teams for checks of plausibility, corrections and possible re-categorisation. Indicators describ-
ing the composition and configuration of the farm (based on area and geometry of polygons
and lines) were then calculated from the habitat types. The habitat types were hierarchically
aggregated to enable the calculation of the other indicators and sub-indicators (Figure 1).
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Figure 1: Hierarchical classification of farm habitat categories. Habitat types (Appendix 1) are grouped into categories at three levels.
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Arable crops were grouped into four habitat categories (1.1.1 to 1.1.4) according to how at-
tractive they are for pollinating insects, and to whether they are annual or perennial crops
(Non-bee attracting annual winter crop, Non-bee attracting annual summer crop, Annual ento-
mophillic crops, Perennials including rotational and productive permanent grassland). To-
gether, they constitute the category of intensively managed farmland without trees (1.1). Con-
ventional vineyards, fruit tree orchards (dwarf trees, no undercropping) and olive groves
(>200 trees per hectare) (1.2.1 to 1.2.4) were aggregated to intensively managed farmland
with woody plants (… with trees, 1.2). Linear grasslands (2.1.1) and extensively managed
areal grasslands (2.1.2) are the extensively managed grassland category without trees (2.1),
whilst various agroforestry systems (mostly silvo-pastoral, mown or grazed, 2.2.1 – 2.2.6)
correspond to extensively managed grassland with trees (2.2). Finally, aquatic habitats and
stone walls, tracks, etc. (2.3.1 to 2.3.4) were grouped as a third category (Other, 2.3) of ex-
tensively managed agricultural habitats. This led then to the two coarse categories of “Intens-
ive agriculture” (1) and of “Semi-natural habitats” (2). Basically, all linear habitats and Annex
1 habitats of the European Habitats Directive were defined as semi-natural. Habitat category
definitions are detailed in Jeanneret et al. (2009).
Grasslands (both productive and semi-natural) were differentiated not only according to their
GHC but also the environmental qualifiers (describing nutrient and moisture status) allocated
during mapping. This enabled the classification of numerous grassland habitats (e.g. mesic-
neutral or wet-acid mixed grass/herb grassland, etc.).
For more specific information on the total number of crops grown on the farm, rather than just
the GHC apparent during the mapping, additional information was collected through inter-
views with the farmers.
2.3 Calculation and selection of habitat indicators
The calculation of indicators and sub-indicators was based on specific groupings of habitat
types, see footnotes of Figure 1. Descriptive statistics were used to examine the indicators’
performance. Box plots were plotted in R (R Development Core Team 2012) to explore the
distribution of the indicator values for each farm, and the median for the case study was calcu-
lated. They allowed to evaluate whether the indicators differentiate between farms of indi-
vidual case study regions and whether this was the case for all farm types or not. Indicators
were evaluated with respect to redundancy, coherence and applicability across Europe, and 12
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unsuitable indicators were discarded (Jeanneret et al., 2012). For each case study region, a
correlation matrix was calculated across all indicators (genetic, species, habitat diversity, farm
management; see Jeanneret et al. 2012, Appendix 3 in particular) and the behaviour of indic-
ators was examined with multivariate statistics and principle component analysis (capacity of
indicators to distinguish between farms, between case study regions and between farming sys-
tems and farm types), The resulting indicators were again audited by the stakeholder advisory
board (Pointereau & Langevin, 2012), and a core set of eight habitat indicators was retained.
3. Results
In the 12 case study regions standardised maps were created for all 195 farms. Farm habitats
were categorized using the hierarchical system from Figure 1. Figure 2 provides examples of
the maps. The 18 candidate indicators were grouped into three categories addressing (i) the
structural composition of the farm, (ii) specific habitat types and (iii) interpreted indicators
(Table 2). Indicator values were computed and plotted for each case study region (Figure 3)
and sub-indicators were calculated (Table 3). Eight indicators were selected as a habitat indic-
ator core set, six indicators would require more research and development to become opera-
tional and four indicators were discarded because they could not be reliably calculated or be-
cause the information they provided was redundant (see Jeanneret et al., 2012).
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Figure 2: Examples of farm maps: Olive farms in Spain (A, B), arable farms in France (C, D), mixed arable – grassland farm in Germany (E), Dehesa farm in Spain (F).
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Table 2: Farm scale habitat indicators which were tested, resulting in eight core indicators, six indicators requiring more research and development and four indicators which were dis-carded due to various reasons.
IndicatorCategory
Indicator Unit Data source
Sub-Indicators Comments
a) Structural composition and configuration of farmCore in-dicator
Habitat Richness
No of habitat types per ha of farm
Habitat map
1) Habitat Richness of Cultiv-ated Crops2) Habitat Richness of Semi-Natural Habitats
Core in-dicator
Habitat Diversity
Shannon diversity index (accounts for both no. of habitat types and the area of each)
Habitat map
1) Habitat Diversity of Cultiv-ated Crops2) Habitat Diversity of Semi-Natural Habitats3) Habitat Diversity of Areal Habitats4) Habitat Diversity of Linear Habitats
Core in-dicator
Patch Size
ha Habitat map
1) Patch Size of Cultivated Crops2) Patch Size of Semi-Natural Habitats
Average patch size of areal habitats
Core in-dicator
Linear Habitats
m/ha Habitat map
1) Grassy Linear Habitats2) Woody Linear Habitats3) Aquatic Linear Habitats4) Wall Linear Habitats
Can be individually calculated for mapped categories
Research indicator
Aggrega-tion
Ratio of total farm size to minimum bounding polygon
Habitat map
Easy to compute from habitat maps but ecological significance unclear for con-consolidated farms
Discarded indicator
Habitat density
N° of habitat patches per ha of farm
Habitat map
Consistent correlation with Patch Size; discarded as redundant
b) Indicators relating to specific habitat typesCore in-dicator
Crop Richness
No of crops per farm / per ha
Farmer inter-views
Most relevant for arable farm types, not applicable for specialist grazing livestock. Requires further adaption for horticultural farm types.
Core in-dicator
Shrub Habitats
% of farm Habitat mapping
Interpretation in context. Can be positive in intensively cultivated areas, but negative in areas of agri-cultural abandonment
Core in-dicator
Tree Hab-itats
% of farm Habitat mapping
1) Cultivated Tree Crops2) Semi-Natural Tree Habitats3) Areal Tree Habitats4) Linear Tree Habitats
Research indicator
Tree Density
N° of trees per ha Habitat mapping
1) Fruit trees2) Timber trees
Was intended to differentiate between intensive and extensive tree crops. Could not be evaluated as only extensive tree crops were present.
Research indicator
Weed % of arable crop field covered by weeds
Vegeta-tion relevé and/or habitat mapping
Would require several visits per season. Upscaling to farm unclear.
Discarded indicator
Arable Land
% of arable land of farm Habitat mapping and/or inter-views
Rotational grassland could not be reliably differentiated from product-ive / intensive permanent grassland.
Discarded indicator
Perman-ent Grass-land
% of permanent grass-land of farm
Habitat mapping and/or inter-views
1) Productive / intensive grassland
2) Semi-natural grassland
Productive / intensive permanent grassland could not be reliably dif-ferentiated from rotational grass-land.
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Table 2: Continued
c) Interpreted indicatorsCore in-dicator
Semi-nat-ural Hab-itats
% of farm Habitat mapping
1) Semi-natural Habitats without Trees2) Semi-natural Habitats with Tree3) Aquatic Semi-natural Hab-itats
Includes all linear habitat and areal habitats classified as semi-natural habitat.
Research indicator
Valuable habitats
% of farm Habitat mapping
Annex 1 habitats could easily be measured, but did not differentiate between farms. Alternative (re-gional) quality criteria could be tested instead.
Research indicator
Quality grassland
% of farm Habitat mapping
A European definition of quality grassland is not meaningful, would require regional thresholds.
Research indicator
Multi-grass swards
% of farm Habitat mapping
A European definition of species rich grassland is not meaningful, would require regional thresholds.
Discarded indicator
Ellenberg values
% of farm with a specific score
Vegeta-tion re-levé, E. value tables
1) Soil moisture2) Soil nitrogen status3) Soil reaction4) Etc.
Ellenberg values are not available for all plants occurring on farmland across Europe.
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Figure 3: Box plots of indicators selected for measuring habitat diversity at the farm scale. See Table 1 for abbreviations of the case study regions and Table 2 for indicator definitions. Bold lines indicate the median, filled points the average value per case study region. Whiskers have a max-imum length of 1.5 × the interquartile range (IQR), i.e. the length of the box. Empty points indicate outliers. For A Habitat Richness and E Crop Richness, two extreme outliers of the region EO were excluded from the figure. Farm size of these two farms was extremely low, i.e. < 1 ha, result-
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AT FR NL BG CH ED NO GB DE HU IT EO
0.0
0.5
1.0
1.5
Habitat Richness
No
of h
abita
t typ
es p
er h
a of
farm
A
AT FR NL BG CH ED NO GB DE HU IT EO
0.0
0.5
1.0
1.5
2.0
Habitat Diversity
Sha
nnon
div
ersi
ty in
dex
B
AT FR NL BG CH ED NO GB DE HU IT EO
0
5
10
15
Patch Size
ha
C
AT FR NL BG CH ED NO GB DE HU IT EO
0
100
200
300
400
Linear Habitats
m/h
a
D
AT FR NL BG CH ED NO GB DE HU IT EO
0.0
0.2
0.4
0.6
Crop Richness
No
of c
rops
per
farm
/per
ha
E
AT FR NL BG CH ED NO GB DE HU IT EO
0
10
20
30
40
50
Shrub Habitats%
of f
arm
F
AT FR NL BG CH ED NO GB DE HU IT EO
0
20
40
60
80
100
Tree Habitats
% o
f far
m
G
AT FR NL BG CH ED NO GB DE HU IT EO
0
20
40
60
80
100
Semi-Natural Habitats
% o
f far
m
H
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277
278
279
280
281
282
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285
286
287
288
289
290
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292
293294295296
ing in extremely high values for indicators calculated per ha farm size. Greyscales indicate farm types, ordered from brightest to darkest as Field crops and horticulture, Specialist grazing livestock, Mixed crops and livestock and Permanent crops.
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Table 3: Sub-indicator values for the 12 case study regions. See Table 1 for abbreviations of the case study regions. For Habitat Richness of Cultiv-ated Crops and Habitat Richness of Semi-Natural Habitats, two extreme outliers of the region EO were excluded (see Figure 3).
Field crops and horticulture Specialist grazing livestock Mixed crops and livestock Permanent cropsIndicator Sub-Indicator AT FR NL BG CH ED NO GB DE HU IT EOHabitat Richness (No
habitat types per ha of farm )
Habitat Richness of Cultivated Crops
median 0.06 0.08 0.21 0 0.24 0 0.16 0.01 0.1 0.05 0.07 0.08
min 0.03 0.01 0.09 0 0.12 0 0.06 0 0.06 0.03 0.02 0
max 0.15 0.25 0.4 0.14 0.79 0 0.38 0.04 0.38 0.53 0.28 0.33Habitat Richness of Semi-Natural Habitats
median 0.09 0.16 0.38 0.4 0.41 0.03 0.71 0.09 0.12 0.05 0.18 0.56
min 0.03 0.04 0 0.16 0.22 0.01 0.34 0.02 0.06 0 0.03 0.25
max 0.35 0.56 1.06 1.88 0.79 0.08 1.25 0.54 0.32 0.27 0.52 1.65Habitat Diversity (Shannon diversity index)
Habitat Diversity of Cultivated Crops
median 1.14 0.84 0.94 0 0.3 0 0.41 0.14 1.46 1.17 0.04 0
min 0.97 0 0 0 0 0 0 0 1.18 0.5 0 0
max 1.33 1.72 1.49 0 0.92 0 0.9 0.73 1.9 1.74 0.54 0.75Habitat Diversity of Semi-Natural Habitats (Shannon)
median 1.07 1.57 1.31 1.15 1.02 1.17 1.53 1.32 1.66 0.66 1.01 0.35
min 0.12 1.05 0 0.06 0.16 0.71 0.94 0.56 0.99 0 0.54 0
max 1.95 2.17 1.73 1.72 1.65 1.92 2.01 1.87 2.12 1.37 1.38 1.3Patch Size (ha) Patch Size of Cultivated Crops median 3.13 4.18 1.5 0 0.8 0 1.2 2.18 1.64 4.65 1.06 0.12
min 1.02 0.99 0.38 0 0.42 0 0.46 0 0.89 1.13 0.37 0
max 13.05 15.52 4.69 0.82 1.81 4.76 2.45 10.41 5.01 26.07 5.78 1.12Patch Size of Semi-Natural Habitats median 0.12 0.05 0.03 0.84 0.02 4.47 0.08 0.48 0.02 0.96 0.03 0.33
min 0.01 0.02 0 0.19 0.01 2.84 0.02 0.07 0.01 0 0.01 0.08
max 0.41 0.23 0.43 2.47 0.14 14.77 0.24 2.14 0.08 23.33 0.18 1.85Linear Habitats (m/ha)
Grassy Linear Habitats median 27.87 82.89 60.18 37.77 84.73 4.29 199.99 24.73 111.33 0 92.37 0
min 6.27 26.71 0 4.6 22.21 0.57 68.84 0 40.78 0 19.46 0
max 51.32 123.23 152.8 98.44 168.48 15.34 315.1 112.48 191.78 21.81 176.21 26.49Woody Linear Habitats median 7.54 70.32 64.54 16.16 17.81 5.11 34.92 58.96 20.11 0.66 40.94 21.93
min 0 5.35 0 0 0 0 6.5 0 1.63 0 7.93 0
max 54.08 128.11 145.06 134.6 57.27 12.1 83.42 166.75 41.92 14.68 99.09 241.8Aquatic Linear Habitats median 0 11.52 8.14 5.15 8.63 12.06 29.19 22.23 18.53 1.14 0 0
min 0 0 0 0 0 6.41 0 3.62 0 0 0 0
max 1.09 50.31 136.9 48.06 71.45 18.21 90.55 106.59 59.76 25.04 116.57 45.13Wall Linear Habitats median 0 0 0 0 0 6.24 0 6.97 0 0 0 97.43
min 0 0 0 0 0 0 0 0 0 0 0 0
max 0 0 0 56.84 7.29 30.46 16.89 64.98 0 0 15.58 442.97Semi-Natural Habit-ats (% of farm)
Semi-Natural Habitats without Trees
median 1.84 2.31 0.91 85.94 0.88 18.11 12.37 17.79 1.86 0.06 1.24 1.13
min 0.25 0.35 0 42.42 0.3 0.07 2.86 0 0.41 0 0.19 0
max 13.02 12.29 12.84 99.52 1.77 62.35 53.03 94.51 4.62 74.58 4.89 33.97Semi-Natural Habitats with Trees median 0.23 2.11 2.16 8.78 1.25 80.11 8.49 5.17 0.67 1.66 1.23 96.36
min 0 0.16 0 0.39 0 33.27 1.94 1.86 0.05 0 0.24 52.29
max 1.62 31.92 89.98 57.58 16.81 99.6 18.37 84.71 1.26 15.28 2.97 100
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4. Discussion
4.1 Farm habitat maps
Farms can be extremely different in terms of size, shape and configuration, even within the
same region (Figures 2A and 2B) or same farm type (Figure 2C and 2D). Of interest also is,
how farms can be contiguous or non-contiguous, how field size varies and the nature (linear
or areal) of the semi-natural habitat (Figures 2E and 2F). The main strength of the mapping
method was that it was based on a concerted protocol which was applicable across a large
geographical gradient. This reduced the problems of subjective landscape interpretation con-
siderably, for example knowing what to map and how to map it (Arnot et al., 2004). It en-
abled the production of general purpose maps that are comparable over a wide geographical
region and for greatly differing farm situations. Such maps can provide ground-truthing in-
formation in the context of a continental or global biodiversity monitoring of “Ecosystem
structure” indicators as EBV, which currently is developed based on remote sensing informa-
tion (mostly satellite, GEO BON 2015). Reliable information collected on site can strongly
improve the validity of maps based on remote sensing, even if high spatial and spectral resol-
ution images are available.
In a more local context, this kind of maps are appealing to both, farmers and stakeholders.
They allow to visualize the current status of farm habitats and – if a time series is established
through repeated mapping – to grasp the changes which occur. Those changes can then be re-
lated to agri-environmental policies as well as to management decisions made by the farmer
due to new technological or business opportunities. The mapping rules can be adapted to
make sure that ecological focus areas are adequately covered. Most likely this would require
some additional information from the farmer, as not all ecological focus areas can clearly be
distinguished in the field.
If on the same farms also species indicators are measured, the maps can be used for develop-
ing a sampling strategy for recording wild species on the farm. This was the case in this pro-
ject, where for species recording a stratified random farm habitat sample was used.
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4.2 Structural composition and configuration of farms
The indicators Habitat Richness, Habitat Diversity, Patch Size and Linear Habitats describe
the structural composition and configuration of the farm (Table 2a). For all four indicators,
values between farms varied considerably, often by a factor of five to ten, within individual
case study regions (Figures 3A – 3D). The indicators, therefore, allow farms to be differenti-
ated between and – if measured repeatedly on an individual farm over several years – are
likely to detect changes in composition.
Higher values of Habitat Richness were reached in some of the grassland case studies, the
Spanish olive case study and also in the Netherlands, where there are comparatively small
plots of different crops / vegetables (Figure 3A). Habitat Diversity values were highest in the
mixed farming systems and lowest in the permanent crop type farms, which are dominated by
either vineyards (Italy) or olive trees (Spain) (Figure 3B). Habitat Richness and Habitat Di-
versity indicators have been associated with high species richness in landscapes dominated by
arable and mixed farming (Billeter et al., 2008). The same was observed on our arable and
mixed farms (Lüscher et al., 2014a, see also Herzog et al., 2012, Figure 6.8). However, we
also observed high species numbers in olive groves, Dehesas (Spain), and in some grassland
dominated farms (e.g. in Bulgaria), which showed relatively low Habitat Richness and Hab-
itat Diversity values. These indicators, therefore, cannot be universally used as predictors for
farmland species richness, but can be important for understanding how farms of a particular
type, or farms in a specific region change over time.
The sub-indicators provided more detailed information and aided in the interpretation of the
main indicator (Table 3). For example, the main Habitat Richness indicator includes all the
habitat types of the farm, i.e. both the crops and semi-natural elements. In some case study re-
gions Habitat Richness is driven mainly by Habitat Richness of Semi-Natural Habitat (grass-
land and Spanish olive farms) whilst in other regions both Habitat Richness of Cultivated
Crops and Habitat Richness of Semi-Natural Habitats are equally represented (arable and
mixed farms, Italian vineyards) (Table 3).
Most Patch Size means were below 5 hectares, except for the grassland farms of the Spanish
Dehesa and the mixed farms of Hungary (Figure 3D). The smallest Patch Size mean occurred
in the Norwegian and Swiss grassland farms and on the olive farms in Spain. A larger mean
was observed for the sub-indicator Patch Size of Cultivated Crops in the arable regions of
Austria and France as well as the mixed farms of Hungary (Table 3). These observations con-
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firm the findings of Herzog et al. (2006) that large farm fields are not necessarily associated
with high farming intensity. The case study farms with the highest average nitrogen fertiliza-
tion and the highest numbers of pesticide applications were in Germany, Switzerland, The
Netherlands, Italy, Germany and Austria, respectively (Table 1; Schneider et al., 2014). The
average Patch Size of Semi-Natural Habitats was small, indicating that many semi-natural ele-
ments are linear. Exceptions were the Spanish Dehesa and to a lesser extent Bulgaria as well
as Hungary (Table 3).
Most farms in the case study regions had a mean Linear Habitats length of between 128 and
184 m/ha (Figure 3D), four regions had <100 m/ha (Austria, Bulgaria, the Spanish Dehesa,
Hungary) and one region >200 m/ha (Norway). In the arable (Austria), mixed farming (Ger-
many), permanent crop (Italy) and grassland farms of Bulgaria, Switzerland and Norway the
highest proportion of linear habitats were Grassy Linear Habitats. Woody Linear Habitats
were the most important linear habitats in Wales and Wall Linear Habitats the most relevant
in the Spanish olive farms (Table 3).
No one single measure alone is adequate to capture farm habitat composition. A set of indicat-
ors is needed for a meaningful interpretation and allows ecological insights (Bailey et al.,
2007ab). For example, the mean Habitat Richness value was relatively low in Austria and
highest in Switzerland. In contrast, the mean Habitat Diversity value was among the higher
values in Austria and among the lowest in Switzerland (Figure 3). This is because the values
are being driven by different factors. The low Habitat Richness value in Austria is largely due
to larger average field sizes of crop fields on the farms (Table 3, Sub-indicator Patch Size of
Cultivated Crops). The mean Habitat Diversity value reflects this and indicates an even distri-
bution of crops and field sizes of the farms (see sub-indicators Habitat Diversity of Cultivated
Crops and Patch Size of Cultivated Crops; Table 3). In contrast, the length of Linear Habitats
was not only lower in Austria than in other case studies but also these were mainly Grassy
Linear Habitats rather than a mixture of different linear habitats (Table 3). These qualities led
to lower Habitat Richness. The Habitat Richness on Swiss farms is mainly being driven by the
semi-natural linear habitats (see sub-indicator Habitat Richness of Semi-Natural Habitats,
Table 3; indicator Linear Habitats, Figure 3D) and the small size of habitats (see Patch Size,
Figure 3C). So although relatively many habitats are represented, most occupy a very small
area and only few a large area, the Habitat Diversity indicator is therefore low.
Frequently, improper use of habitat indicators leads to problems with interpretation (Neel et
al., 2004) and misunderstandings (Li & Wu, 2004). The Spanish Dehesa farms, for example, 22
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have the lowest average values of Habitat Richness (0.04 / ha) and the Norwegian grassland
farms the highest (0.80 / ha). However, the lower Habitat Richness in the Dehesa is because
these farms are both large (mean size 483 ha, compared to 15.7 ha in Norway, see Table 1)
and have a much larger mean habitat Patch Size compared to the farms of other regions (Fig-
ure 3D). Therefore, the indicators should be used as a set and comparisons across regions are
only meaningful if the areas investigated have similar qualities.
Two farm composition indicators were discarded from the core set (Table 2a). The Aggrega-
tion index is easy to compute and enables the spatial distribution of the elements on the farm
to be considered as it identifies if the farm fields are widely dispersed or aggregated. As such
it may contribute to characterize farm geometry. However, the ecological significance of this
measure is unclear as farms which are widely dispersed will be intertwined with other farm
and non-farm habitat. This is one of the challenges of the farm scale and further research is
needed to assess the relevance of this indicator. Habitat Density, i.e. the number of habitats
per ha, was correlated with Patch Size. We rejected one of the tow redundant indicators and
kept Patch Size as it is intuitively easier to understand and more appropriate for farms com-
posed of scattered individual plots. High levels of redundancy between habitat indicators are
common as aspects of landscape pattern are inter-related (Riitters et al., 1996; Bogaert et al.,
2002; Li & Wu, 2004).
4.3 Specific farm habitats
Three indicators were retained that address specific farm habitats: Crop Richness, Shrub Hab-
itats and Tree Habitats (Table 2b). Crop Richness values were highest in the mixed farms of
Germany (Figure 3E). This indicator is not applicable to Specialist grazing livestock farms,
which don’t grow crops. Data collection failed in the Netherlands because the complex crop-
ping pattern of horticultural farms (several different crops of varying shares within the same
year, often at short notice) meant that reliable data could not be collected during the farmer
interviews. The indicator would need to be refined for horticultural farms.
Woody elements (shrubs, trees) on farms are fundamentally different from crop fields or
grassland and as permanent, over-wintering structures provide habitat for various arthropods,
birds and small mammals that otherwise could not exist on the farm (e.g. Hinsley & Bellamy
2000; Holland & Fahrig 2000; Schmidt & Tscharntke, 2005; Bailey et al., 2010). They are
also key elements for the perception of landscapes and are highly valued by both farmers and 23
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non-farmers for aesthetic reasons (Junge et al., 2011). This is why the two indicators Shrub
Habitats and Tree Habitats were tested. In fact, the percentage of Shrub Habitats tended to be
low in the intensively used regions such as the horticultural and mixed farms of Austria,
France, The Netherlands and Germany (Figure 3F). Values were also low on the Italian wine
farms and the Swiss and Norwegian grassland farms. In contrast, there was generally a high
share of Shrub Habitats in the Spanish Dehesa, Wales and Bulgaria and for some farms the
values were close to 50%. The percentage of Tree Habitats was not computed for the perman-
ent crop farms (e.g. olive farms in Spain), because they tend towards similarly high values
and the indicator does not contribute to differentiate between them. Structuring by woody ele-
ments on farms was most apparent on the grassland farms of Bulgaria, Norway, Wales and
Switzerland. The lowest values were recorded on the arable and mixed farms (Figure 3G). It
is essential to interpret these results in regional context. On intensively managed farms, an in-
crease of Shrub Habitats or Tree Habitats (at a low level) can be interpreted as habitat diversi-
fication through the creation of new habitats, which is expected to be beneficial for species di-
versity. A decrease can signal loss of traditional farm habitat (e.g. traditional orchards). On
extensively managed farms, such as in the marginal regions of Spanish Dehesa, Wales and
Bulgaria, high Shrub Habitat values may indicate a trend towards abandonment, which will
result in a loss of farmland biodiversity as farm habitat decreases and the shrub evolves to-
wards forest stands (Brown, 1991).
Two more indicators would require further research (Table 2b). Tree Density could probably
be used to differentiate between intensive and extensive orchards and olive plantations. This
was not possible to test in this study as all plantations were extensive according to our defini-
tions (Figure 1). The percentage of Weeds in crop fields could be measured through vegeta-
tion surveys or during habitat mapping. Whilst each farm was only visited once for habitat
and vegetation mapping, an indicator related to weeds would require several surveys during
the season to capture a wider representation of the weed species. The up-scaling of the meas-
ure from plot to farm scale would also need careful consideration.
The indicators Arable Land and Permanent Grassland had to be discarded because they could
not be reliably measured (Table 2b). We defined grassland as permanent if it was in place for
five years or more. However, contrary to our expectations, it proved difficult to differentiate
sown grassland (arable crop) from permanent grassland during the field mapping and also
during interviews with the farmers. Farmers who did not keep records dating back over sev-
eral years were not able to indicate the year of establishment of sown but older grasslands
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which would qualify as permanent. Moreover, in some countries agri-environmental pay-
ments are linked to permanent grassland, which may distort the farmers’ response. This obser-
vation is relevant in the context of the “greening” policy of the European Common Agricul-
tural Policy, which aims at the conservation of permanent grassland. The success of this
measure can probably only be verified within a certain error. Reliability would increase over
time if, in a monitoring, the same farm were repeatedly mapped and the sequence of the util-
isation of individual plots were known over time.
4.4 Interpreted farm habitat indicators
Several indicators were tested which attempt to give value (in terms of nature protection) to
farm habitats because there is a demand by stakeholders for interpreted information, which
can be easily communicated to farmers and policy makers, such as High Nature Value (HNV)
farming (Oppermann et al. 2012, Strohbach et al. 2015). Whilst HNV farming is a regional
indicator, we tested four potential farm scale indicators which address the “ecological value”
of habitats (Table 2c). The percentage of Semi-Natural Habitats was the indicator which was
retained in this category. It was highest in the farms of Bulgaria (grassland) and of the two
Spain regions (olive farms and Dehesa) (Figure 3H). These farms consisted almost entirely of
extensive agricultural and semi-natural habitats. The Bulgarian grasslands are located at relat-
ively high altitude (above 950 m a.s.l.) and are extensively managed (sheep pastures with low
stocking density <0.6 livestock units/ha), the Spanish olive farms are all traditional olive
orchards with tree densities <200 trees/ha and the Dehesas are classified as Annex 1 habitats
in the European Habitat Directive. For such regions, the main indicator is not very informat-
ive as it does not differentiate between the farms within the region. However, the sub-indicat-
ors that divide grassy and woody habitat allow the farms to be differentiated (Sub-indicators
Semi-Natural Habitats without Trees, Semi-Natural Habitats with Trees; Table 3). In the other
case study regions the farms differ from each other in their percentage of Semi-Natural Habit-
ats. Habitat values tended to be higher on the grassland farms (Wales, Norway) and the mixed
farms of Hungary (Figure 3H). The values were lower in the Swiss grassland farms, the
mixed farms of Germany, the vineyards of Italy and the arable farms of Austria, France and
the Netherlands. These farms are more intensive and the main semi-natural habitat elements
were linear. Changes in Semi-Natural Habitats must be interpreted in conjunction with the
other habitat indicators. Increases in Semi-Natural Habitats could indicate habitat restoration
measures, e.g. in the context of agri-environmental schemes or increases in woody structures 25
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to reduce erosion in arable regions. However, it could also indicate abandonment, which
could be detected e.g. by an increase in Shrub Habitats. The indicator will also be more sensit-
ive to changes of the areal semi-natural elements than of linear features such as hedgerows,
field borders and ditches, which cover only small portions of the farm. Thus, the indicator
Linear Habitats both complements the Semi-Natural Habitats indicator and is essential to ob-
serve changes of habitat on the farm.
From a scientific point of view, however, this indicator is problematic as it is very sensitive to
the criteria used for classifying a habitat as semi-natural. Whilst for some habitats this distinc-
tion is clear, e.g. dry meadow vs. maize field, there are intermediate habitats that can be clas-
sified either way. Grasslands in particular tend to show a gradient in terms of management
and species richness. We attempted a categorization at the European level. However, national
categorizations may be more relevant and meaningful to farmers and stakeholders. In Switzer-
land, for example, ecological focus areas (unfertilized grasslands with late cut obligation,
comprising about 10% of the meadows of the Swiss farms) are usually regarded as semi-nat-
ural habitat, but did not meet the European standards defined here. In Norway, grazed wood-
lands were defined as semi-natural, being the clear result of a combination of natural and agri-
cultural influences on the vegetation. However, the degree of agricultural influence varied
with grazing pressure and time since last grazed; further complicated by the possible effects
of grazing by non-domestic animals (deer, moose).
Four indicators were not taken forward (Table 2c). The indicator Valuable Habitats was
defined as Annex 1 habitats according to the European Habitats Directive. Whilst it could eas-
ily be measured on the farms, it did not differentiate between farms as – in the 12 case study
regions which were investigated – Annex 1 habitats were either not present at all or almost the
entire farm was classified as Annex 1 habitat (Spanish Dehesa). So, this indicator is not useful
for stakeholders to apply within one of the studied region. It would need to be tested in other
case study regions or other criteria would need to be used to qualify Valuable Habitats, e.g.
target habitats, national priority habitats. If the indicator were re-defined, this would mean
that it would be regionally or nationally relevant but not applicable at the European scale.
Similarly, the indicators Quality Grassland and Multigrass Swards are more appropriate at the
regional and possibly national scale rather than at the European level. They would need care-
ful definition and then could either be incorporated into the habitat mapping or derived from
vegetation surveys. The Ellenberg Values were discarded as indicator values are not systemat-
ically available across Europe (Ellenberg, 1988).
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4.5 Practical considerations
Indicator values depend strongly on the definition of the habitats and the habitat mapping
method because habitat indicators are very sensitive to the effects of thematic and spatial res-
olution (e.g. Wu et al., 2002; Wu, 2004; Baldwin et al., 2004; Bailey et al, 2007ab; Cain et al.,
2007). By controlling for resolution we could make sure that differing indicator values were
actually due to different habitat properties.
Seven core set indicators require field mapping and preferably the application of GIS to calcu-
late areas and lengths of the habitats on the farm. In countries where GIS is not readily avail-
able, adaptions to the field recording could be made in order to collect data about the area and
length of habitat during mapping. The indicator Crop Richness requires no habitat mapping
and is collected through an interview with the farmer.
An important consideration when measuring habitat diversity on farms is the cost of measure-
ment. A monitoring program needs to be sound, affordable and it should be possible for the
decision-makers to allocate their financial resources wisely (Targetti et al., 2015; Herzog and
Franklin, 2016). For this a cost-analysis is essential (Caughlan & Oakley, 2001) but there are
few cost-effectiveness studies of biodiversity measurement and mostly those available are
based de facto on proxies, ex post estimations or a limited area (e.g. Qi et al., 2008; Schmeller
& Henle, 2008). Direct costs for indicator measurement were assessed in this study. Mapping
an average farm of 85 hectares required 2.3 person days for mapping and indicator calculation
(Targetti et al., 2014). Obviously, the mapping effort will depend on the size and complexity
of the farm, which varies considerably across Europe.
One of the drawbacks of evaluating habitats at the farm scale as compared to the landscape
scale is that it does not allow for an assessment of the spatial configuration of habitats or for
the influence of habitats in the surrounding area. This is a serious limitation as it is widely ac-
knowledged that the spatial arrangement of habitats, their isolation and fragmentation act on
farmland species diversity (e.g. Hagen et al., 2012, but see Fahrig, 2013). This limits the pos-
sibilities for joint interpretations of habitat and species diversity indicators. Another limitation
might be – although it was not tested here – that over time farms can be considered as “mov-
ing targets”. Whilst landscape samples (square kilometres for example) remain spatially con-
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stant over time, farms tend to change in spatial extent. Land may be sold or bought/rented,
which affects the farm boundary, or the farm may even be entirely abandoned. This needs to
be taken into consideration when repeated measurements over time are planned.
5. Conclusions
To our knowledge, this is the first attempt to map habitats of individual farms across Europe
and over several farm types. The general purpose maps of the farms are quite detailed in terms
of spatial and thematic resolution. Combining such maps with remote sensed information can
facilitate and improve the interpretation of aerial or satellite images. At the same time, the re-
motely sensed images may be used to upscale the information obtained from the maps to lar-
ger regions. Therefore, the approach and the indicators tested here can complement the devel-
opment of EBV such as the Biodiversity Habitat Index, which is geared towards the global
monitoring of biodiversity based on globally available satellite information (Geo Bon, 2015).
For global applications, however, our mapping procedure would need to be further developed
to additional climatic conditions and farm types. For example, whilst minor adaptations, ac-
counting e.g. for the grain of the landscape, allowed mapping farms also in the Ukraine and in
Tunisia, mapping complex and tropical continuous intercropping farming systems in Uganda
proved much more difficult (Riedel, 2012).
The eight indicators are a minimum set providing non-redundant information on the composi-
tion of the farm, inform about specific habitat types and about the share of semi-natural habit-
ats. If interpreted in context and as a set, they allow the habitat status of the farm to be tracked
over time. In many cases, sub-indicators should also be evaluated and they may even be more
informative.
Farm scale habitat and biodiversity indicators (state indicators) can be directly related to farm
management (pressure indicators) and are therefore particularly useful to evaluate agri-envir-
onmental policies (response) (Geijzendorffer et al., 2016). The habitat mapping approach
proposed here is flexible enough to evaluate ecological focus areas and, with repeated meas-
urements, the evolution of (permanent) grassland can be tracked. The habitat indicators can be
combined with species indicators recorded on the same farm, which allows to evaluate
whether biodiversity goals are reached and to understand the relevant mechanisms.
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Acknowledgments
We are grateful to all farmers who allowed access to their fields and provided information on land management. This work was partly funded by the European Union through the FP7 project BioBio (Indicators for biodiversity in organic and low-input farming systems; www.biobio-indicators.org) and the Austrian Ministry for Science and Research. András Báldi was partly supported by the MTA Lendület Program. Josef Mayr and Thomas Hüb-ner supported data acquisition and digitising maps in the Austrian case study region. We acknowledge the care-ful comments of two anonymous reviewers, which helped to considerably improve an earlier version of the ma-nuscript.
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Appendix 1: Farm habitats recorded in the twelve case study regions
Coding and terminology were adapted from the EBONE European habitat mapping guidelines http://www.wageningenur.nl/en/Expertise-Services/
Research-Institutes/alterra/Projects/EBONE-2.htm (accessed 20. May 2016)
Code Description1.1.1 Annual winter crops
CAN1Not entomophilic and/or bee attracting annual winter crops (winter oats, winter barley, winter wheat, beans, triti-cale, rye, etc.)
1.1.2 Annual summer crops
CAN2Not entomophilic and/or bee attracting annual spring crops (spring oats, spring barley, spring wheat, peas, beans, lettuce, etc.)
1.1.3 Annual entomophilic crops
CFLEntomophilic and/or bee attracting annual crops (oil seed rape, sunflower, maize, soya, cucumber, tomatoes, potato, strawberries, etc.)
1.1.4 Perennial crops and grasslandCFO Perennials (e.g. asparagus, rotational grassland, lucerne, etc.)CHE Caesposite hemicryptophytesCHE/GEO Caesposite hemicryptophytes and geophytesCHE/THE_3.4 Caesposite hemicryptophytes and therophytes on seasonally wet, basic soilCHE/THE_5.3 Caesposite hemicryptophytes and therophytes on mesic, neutral soilCHE/THE_5.4 Caesposite hemicryptophytes and therophytes on mesic, basic soilCHE/THE_5.5 Caesposite hemicryptophytes and therophytes on mesic, low saline soilCHE/THE_6.3 Caesposite hemicryptophytes and therophytes on dry, neutral soilCHE/THE_6.4 Caesposite hemicryptophytes and therophytes on dry, basic soilCHE/THE_6.5 Caesposite hemicryptophytes and therophytes on dry, low saline soilCHE/THE_7.3 Caesposite hemicryptophytes and therophytes on very dry, neutral soilCHE/THE_7.4 Caesposite hemicryptophytes and therophytes on very dry, basic soilCHE_2.5 Caesposite hemicryptophytes on waterlogged, low saline soil
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CHE_3.2 Caesposite hemicryptophytes on seasonally wet, acid soilCHE_3.4 Caesposite hemicryptophytes on seasonally wet, basic soilCHE_3.5 Caesposite hemicryptophytes on seasonally wet, low saline soilCHE_4.1 Caesposite hemicryptophytes on wet, eutrophic soilCHE_4.3 Caesposite hemicryptophytes on wet, neutral soilCHE_4.4 Caesposite hemicryptophytes on wet, basic soilCHE_5.1 Caesposite hemicryptophytes on mesic, eutrophic soilCHE_5.2 Caesposite hemicryptophytes on mesic, acid soilCHE_5.3 Caesposite hemicryptophytes on mesic, neutral soilCHE_5.4 Caesposite hemicryptophytes on mesic, basic soilCHE_5.5 Caesposite hemicryptophytes on mesic, low saline soilCHE_6.3 Caesposite hemicryptophytes on dry, neutral soilCHE_6.5 Caesposite hemicryptophytes on dry, low saline soilCHE_OPE_6.3 Caesposite hemicryptophytes on dry, neutral soil: tree cover 1 - 10%HEL_2.4 Helopohytes on waterlogged, basic soilHEL_3.2 Helopohytes on seasonally wet, acid soilHEL_3.5 Helopohytes on seasonally wet, low saline soilLHE Leafy hemicryptophytesLHE/CHE Leafy hemicryptophytes and caesposite hemicryptophytesLHE/CHE_2.4 Leafy hemicryptophytes and caesposite hemicryptophytes on waterlogged, basic soilLHE/CHE_3.2 Leafy hemicryptophytes and caesposite hemicryptophytes on seasonally wet, acid soilLHE/CHE_3.4 Leafy hemicryptophytes and caesposite hemicryptophytes on seasonally wet, basic soilLHE/CHE_3.5 Leafy hemicryptophytes and caesposite hemicryptophytes on seasonally wet, low saline soilLHE/CHE_4.1 Leafy hemicryptophytes and caesposite hemicryptophytes on wet, eutrophic soilLHE/CHE_4.2 Leafy hemicryptophytes and caesposite hemicryptophytes on wet, acid soilLHE/CHE_4.3 Leafy hemicryptophytes and caesposite hemicryptophytes on wet, neutral soilLHE/CHE_4.5 Leafy hemicryptophytes and caesposite hemicryptophytes on wet, low saline soil
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Appendix 1 continued
LHE/CHE_5.1 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, eutrophic soilLHE/CHE_5.2 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, acid soilLHE/CHE_5.3 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, neutral soilLHE/CHE_5.4 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, basic soilLHE/CHE_5.5 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, low saline soilLHE/CHE_6.3 Leafy hemicryptophytes and caesposite hemicryptophytes on dry, neutral soilLHE/CHE_6.4 Leafy hemicryptophytes and caesposite hemicryptophytes on dry, basic soilLHE/CHE_6.7 Leafy hemicryptophytes and caesposite hemicryptophytes on dry, high saline soilLHE/THE Leafy hemicryptophytes and therophytesLHE_4.2 Leafy hemicryptophytes on wet, acid soilLHE_5.1 Leafy hemicryptophytes on mesic, eutrophic soilLHE_5.3 Leafy hemicryptophytes on mesic, neutral soilLHE_6.3 Leafy hemicryptophytes on dry, neutral soilTHE_5.3 Therophytes on mesic, neutral soilTHE_5.4 Therophytes on mesic, basic soilTHE_6.3 Therophytes on dry, neutral soilTHE_7.4 Therophytes on very dry, basic soil1.1.? Crops not defined (not visible on the field at the moment of mapping)CRO Cultivated herbaceous cropsEAR EarthSPA Cultivated bare ground, i.e. bare fallow or recently ploughed landSPA/CRO Cultivated bare ground and cultivated herbaceous crops1.2.1 Vines intensiveWOC_VIN Woody crops vines1.2.2 Orchards >100 trees/haWOC_FRU Woody crops fruit trees
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1.2.3 Olive groves >200 trees/haWOC_OLI Woody crops olives, soil covered by herbaceous vegetation1.2.4 OtherCHE_OPE_5.3 Caesposite hemicryptophytes on mesic, neutral soil: tree cover 1 - 10%LHE/CHE_OPE_5.1 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, eutrophic soil: tree cover 1 - 10%LHE/CHE_OPE_5.3 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, neutral soil: tree cover 1 - 10%2.1.1 Grasslands linear (e.g. field margin)GST Grass stripsHST Herbaceous stripsTGS Private track grass stripsTHS Private track herbaceous strips2.1.2 Grasslands arealBOU BouldersCHE Caesposite hemicryptophytesCHE/CRY Caesposite hemicryptophytes and cryptogamsCHE/GEO Caesposite hemicryptophytes and geophytesCHE/THE_5.2 Caesposite hemicryptophytes and therophytes on mesic, acid soilCHE/THE_5.3 Caesposite hemicryptophytes and therophytes on mesic, neutral soilCHE/THE_6.3 Caesposite hemicryptophytes and therophytes on dry, neutral soilCHE_3.6 Caesposite hemicryptophytes on seasonally wet, medium saline soilCHE_4.2 Caesposite hemicryptophytes on wet, acid soilCHE_4.3 Caesposite hemicryptophytes on wet, neutral soilCHE_4.4 Caesposite hemicryptophytes on wet, basic soilCHE_4.5 Caesposite hemicryptophytes on wet, low saline soilCHE_5.1 Caesposite hemicryptophytes on mesic, eutrophic soilCHE_5.2 Caesposite hemicryptophytes on mesic, acid soilCHE_5.3 Caesposite hemicryptophytes on mesic, neutral soil
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CHE_6.2 Caesposite hemicryptophytes on dry, acid soilCHE_6.3 Caesposite hemicryptophytes on dry, neutral soilCHE_6.4 Caesposite hemicryptophytes on dry, basic soilEAR EarthGEO GeophytesHEL HelopohytesHEL_1.4 Helopohytes on aquatic, basic soilHEL_1.5 Helopohytes on aquatic, low saline soilHEL_2.4 Helopohytes on waterlogged, basic soilHEL_2.5 Helopohytes on waterlogged, low saline soilHEL_2.6 Helopohytes on waterlogged, medium saline soilHEL_3.5 Helopohytes on seasonally wet, low saline soilHEL_4.2 Helopohytes on wet, acid soilHEL_4.3 Helopohytes on wet, neutral soilLHE Leafy hemicryptophytesLHE/CHE Leafy hemicryptophytes and caesposite hemicryptophytesLHE/CHE_3.1 Leafy hemicryptophytes and caesposite hemicryptophytes on seasonally wet, eutrophic soilLHE/CHE_3.2 Leafy hemicryptophytes and caesposite hemicryptophytes on seasonally wet, acid soilLHE/CHE_3.3 Leafy hemicryptophytes and caesposite hemicryptophytes on seasonally wet, neutral soilLHE/CHE_3.5 Leafy hemicryptophytes and caesposite hemicryptophytes on seasonally wet, low saline soilLHE/CHE_4.2 Leafy hemicryptophytes and caesposite hemicryptophytes on wet, acid soilLHE/CHE_4.3 Leafy hemicryptophytes and caesposite hemicryptophytes on wet, neutral soilLHE/CHE_4.4 Leafy hemicryptophytes and caesposite hemicryptophytes on wet, basic soilLHE/CHE_5.1 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, eutrophic soilLHE/CHE_5.2 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, acid soilLHE/CHE_5.3 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, neutral soilLHE/CHE_5.7 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, high saline soil
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LHE/CHE_6.2 Leafy hemicryptophytes and caesposite hemicryptophytes on dry, acid soilLHE/CHE_6.3 Leafy hemicryptophytes and caesposite hemicryptophytes on dry, neutral soilLHE/CHE_6.4 Leafy hemicryptophytes and caesposite hemicryptophytes on dry, basic soilLHE/THE Leafy hemicryptophytes and therophytesLHE_4.1 Leafy hemicryptophytes on wet, eutrophic soilLHE_4.3 Leafy hemicryptophytes on wet, neutral soilLHE_5.1 Leafy hemicryptophytes on mesic, eutrophic soilLHE_5.2 Leafy hemicryptophytes on mesic, acid soilLHE_5.3 Leafy hemicryptophytes on mesic, neutral soilLHE_5.4 Leafy hemicryptophytes on mesic, basic soilLHE_6.4 Leafy hemicryptophytes on dry, basic soilSAN SandSPA Cultivated bare ground, i.e. bare fallow or recently ploughed landSTO StonesTHE TherophytesTHE_5.2 Therophytes on mesic, acid soilTHE_5.3 Therophytes on mesic, neutral soilTHE_6.3 Therophytes on dry, neutral soil2.2.2 Orchards <100 trees/haWOC_FRU Woody crops fruit treesWOC_INT Woody crops and intercropping2.2.3 Olive groves <200 trees/haWOC_EAR Woody crop olives with bare groundWOC_FRU Woody crops fruit treesWOC_INT Woody crops and intercroppingWOC_MPH Woody crops olives and mid phanerophytesWOC_OLI Woody crops olives, soil covered by herbaceous vegetationWOC_TER Woody crops olives on terracesWOC_VIN Woody crops vines
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2.2.4 DehesaCHE/THE_OPE_6.3 Caesposite hemicryptophytes and therophytes on dry, neutral soil: tree cover 1 - 10%CHE_OPE_5.3 Caesposite hemicryptophytes on mesic, neutral soil: tree cover 1 - 10%FPH/DEC Forest phanerophytes winter deciduousFPH/EVR Forest phanerophytes evergreenMPH/DEC Mid phanerophytes winter deciduousMPH/EVR Mid phanerophytes evergreenSCH/DEC Shrubby chamaephytes winter deciduousTHE_OPE_6.3 Therophytes on dry, neutral soil: tree cover 1 - 10%TPH/EVR Tall phanerophytes evergreen2.2.5 HedgerowsHED HedgesLSC Lines of scrubLTR Lines of treesSRH Species rich hedges2.2.6 Small woods <800 sqmCHE_OPE_5.2 Caesposite hemicryptophytes on mesic, acid soil: tree cover 1 - 10%CHE_OPE_5.3 Caesposite hemicryptophytes on mesic, neutral soil: tree cover 1 - 10%FPH/CON Forest phanerophytes coniferousFPH/DEC Forest phanerophytes winter deciduousFPH/DEC/CON Forest phanerophytes winter deciduous and coniferousFPH/EVR Forest phanerophytes evergreenGEO_OPE Geophytes: tree cover 1 - 10%LHE/CHE_OPE_5.2 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, acid soil: tree cover 1 - 10%LHE/CHE_OPE_5.4 Leafy hemicryptophytes and caesposite hemicryptophytes on mesic, basic soil: tree cover 1 - 10%LHE/CHE_OPE_6.2 Leafy hemicryptophytes and caesposite hemicryptophytes on dry, acid soil: tree cover 1 - 10%LHE/CHE_OPE_6.3 Leafy hemicryptophytes and caesposite hemicryptophytes on dry, neutral soil: tree cover 1 - 10%
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LHE_OPE_5.4 Leafy hemicryptophytes on mesic, basic soil: tree cover 1 - 10%LPH/CON Low phanerophytes coniferousLPH/DEC Low phanerophytes winter deciduousLPH/EVR Low phanerophytes evergreenLPH/NLE Low phanerophytes non-leafy evergreenMPH/CON Mid phanerophytes coniferousMPH/DEC Mid phanerophytes winter deciduousMPH/DEC/CON Mid phanerophytes winter deciduous and coniferousMPH/EVR Mid phanerophytes evergreenMPH/NLE Mid phanerophytes non-leafy evergreenSCH Shrubby chamaephytesSCH/DEC Shrubby chamaephytes winter deciduousSCH/EVR Shrubby chamaephytes evergreenTPH/CON Tall phanerophytes coniferousTPH/DEC Tall phanerophytes winter deciduousTPH/EVR Tall phanerophytes evergreen2.3.1 Aquatic linearPOND Pond (mapped as linear element because of ecological significance but size mostly below minimum mapping unit)WAT Water edges2.3.2 Aquatic areal (e.g. pond <800 sqm)AQU AquaticEHY Emergent hydrophytesHEL_1.5 Helopohytes on aquatic, low saline soilHEL_2.3 Helopohytes on waterlogged, neutral soilHEL_2.4 Helopohytes on waterlogged, basic soilHEL_2.5 Helopohytes on waterlogged, low saline soilHEL_2.6 Helopohytes on waterlogged, medium saline soil
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HEL_3.2 Helopohytes on seasonally wet, acid soilHEL_3.4 Helopohytes on seasonally wet, basic soilHEL_3.5 Helopohytes on seasonally wet, low saline soilHEL_4.2 Helopohytes on wet, acid soilHEL_4.7 Helopohytes on wet, high saline soilHEL_5.4 Helopohytes on mesic, basic soilHEL_6.3 Helopohytes on dry, neutral soilSHY Submerged hydrophytesSHY/HEL Submerged hydrophytes and helophytes2.3.3 Stone wallsWAL Walls
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