# Identification of sentinel plant species for evaluating phytotoxicity of veterinary antibiotics in Mediterranean Europe

Rosa García, Jesús Martínez-Fernández, Antonio Rodríguez, Ana de la Torre

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Rosa García, Jesús Martínez-Fernández, Antonio Rodríguez, Ana de la Torre. Identification of sentinel plant species for evaluating phytotoxicity of veterinary antibiotics in Mediterranean Europe. Environmental Sciences Europe, 2022, 34 (1), pp.37. 10.1186/s12302-022-00608-0. hal-05105738

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# Identification of sentinel plant species for evaluating phytotoxicity of veterinary antibiotics in Mediterranean Europe

Rosa M. García¹ ID, Jesús Martínez-Fernández² ID, Antonio Rodríguez<fnref n="3" /> ID and Ana de la Torre<fnref n="3" />* ID

**Abstract**

**Background:** Antibiotics used to treat livestock species enter agricultural fields when they are excreted by grazing animals or are present in manure that is added to fields as fertiliser. In the European Union, the potential effects of such antibiotics on terrestrial plants must be evaluated following the standardised OECD 208 method, which specifies the crop and wild species that should serve as "sentinels" for assessing antibiotic exposure. The present study aimed to compare this approved list of sentinel species against crop and wild plant species actually present in agricultural and pasture lands in Mediterranean Europe in order to identify the most appropriate sentinel plants for the region. The study focused on Spain as a region representative of Mediterranean Europe. Georeferenced layers for wild plant species and cultivated areas (crops), livestock density and land cover were combined, and then selection criteria were applied, leading to the identification of sentinel crop and wild species for crop land and pasture scenarios.

**Results:** In the crop land scenario, the sentinel crop species were barley (*Hordeum vulgare* L.), wheat (*Triticum* spp.), corn (Zea mays L), sunflower (*Helianthus annuus* L.), dried pea (*Pisum sativum* L.), alfalfa (*Medicago sativa* L.), vetch (*Vicia sativa* L.), oilseed rape (*Brassica napus* L.) and sugar beet (*Saccharum officinarum* L.), all of them listed in the OECD 208 method with the exception of alfalfa; the sentinel wild species were *Papaver rhoeas* L., *Galium aparine* L. and *Chenopodium album* L. In the pasture scenario, sentinel wild species were *Bromus tectorum* L., *Agrostis capillaris* L., *Trifolium pratense* L., *Lotus corniculatus* L. and *Galium aparine* L. The following common weed species in field boundaries or in pasture lands also emerged as potential sentinel species for risk assessment, even though they are not listed in the OECD 208 method: *Sonchus oleraceus* L., *Avena sterilis* L., *Dactylis glomerata* L., *Hordeum murinum* L. and *Lolium rigidum* Gaudin.

**Conclusions:** The sentinel species identified in this study may be useful in risk assessment procedures covering the Mediterranean Europe. The method developed for this study could be applied to identify sentinel species for other representative agroclimatic regions in Europe (such as Atlantic and Continental).

**Keywords:** Environmental risk assessment, Agricultural land, Pasture land, Spatial analysis, Crop, Wild

**Background**

Agricultural land is at high risk of contamination by anti-
biotics used to treat livestock species, and the antibiotics
can harm crop plants [1–8] and wild plants [4, 5, 8, 9].

Antibiotics are poorly absorbed by animals, so 30–90% is excreted in manure that is added to the field immediately by grazing animals or later when it is added to fields as fertiliser [10–12]. This is a huge-scale process. For instance the European Union and UK produced more than 1.4 billion of tonnes of manure from animal farming during the period 2016–2019, from which 90% was applied to soil as organic fertiliser [13]. Antibiotics pass from the manure into the soil, where they have been

<figure><img src="image_4.png" /></figure>

<footnote>*Correspondence: torre@inia.es</footnote>

<footnote n="3">³ Animal Health Research Centre, INIA-CISA/CSIC, Valdeolmos, Madrid, Spain</footnote>

<footnote>Full list of author information is available at the end of the article</footnote>
reported to reach maximum concentrations between
$4 \times 10^{-4}$ and 50,000 µg/kg [14]. Studies often using higher
antibiotic concentrations than those expected in agri-
cultural soil [15, 16] have suggested that the compounds
can harm plants mainly by delaying or inhibiting germi-
nation; inhibiting enzyme activity; stunting growth of
shoots, roots, and stems; reducing the number of leaves
and biomass production; and inducing necrosis and
death [16, 17]. Minden et al. [17] observed delayed ger-
mination of crop and wild species after exposure to anti-
biotic concentrations typically found in agricultural and
grassland soil. Ultimately, antibiotics may reduce plant
species richness, abundance and/or diversity in treated
fields as well as adjacent habitats, such as field margins
and grasslands [17], with secondary effects on species at
higher trophic levels [18]. All these effects may be poten-
tially widespread in the European Union, since 37% of its
total land is used for agriculture, with 12% of the total
land serving as crop land to grow plants for human and
animal consumption, while 25% serves as pasture land for
grazing animals [19].

The EU mandates assessment of the risk that antibi-
otics used to treat livestock may exert environmental
effects (Directive 92/18/EC [20]). This assessment should
be carried out according to the standardised OECD 208
method, based on sentinel terrestrial plant species drawn
from an approved list of 32 crop species and 52 wild spe-
cies. Current recommendations are to include four or
five dicotyledonous (dicot) and two or three monocoty-
ledonous (monocot) crop species in the risk assessment
[21], in order to adequately represent the types of plants
grown on agricultural land fertilised with manure. How-
ever, we [22] and others [23] have argued for inclusion of
the Poaceae family as the most representative crop fam-
ily, given that cereals for human and animal consumption
are grown on 50% of EU agricultural land [24]. We have
also argued for including wild species in risk assessments
in order to ensure the broadest protection of plant biodi-
versity [22].

Plant biodiversity plays a critical role in biogeochemical
cycles and provides important ecosystem services [25–
27]. Its protection against livestock antibiotics spread by
agricultural practices is highly justified since agriculture
in general may be one of the largest contributor to bio-
diversity loss [28]. This is due to its extension (agricul-
ture takes up a third of the world’s surface land [29]), its
expansion (about 300% since 1970 worldwide [29]) and
its intensification by mechanised agricultural practices,
the use of fertilisers and pesticides and the cultivation of
selected plant species and varieties in extensive, homoge-
neous landscapes [30–33]. For example, a shift in many
EU countries from semi-natural to cultivated grasslands
that serve as pastures and meadows for livestock grazing

or animal feed production has reduced plant biodiversity
[31].

In the present study, we drew on geographical informa-
tion system mapping data to catalogue all crop species
and wild plant species listed in the OECD 208 method
on agricultural and pasture lands in a representative
area of Mediterranean Europe. Then we applied selec-
tion criteria to the catalogue in order to identify the
species potentially most suitable as sentinels for inclu-
sion in risk assessments of environmental effects from
antibiotics used to treat livestock. We selected Spain as
a representative country in Mediterranean Europe. Most
(85.5%) of the country is found in the Mediterranean
biome, and the remaining 14.5% belong to the temperate
biome [34], which covers the north of the Iberian Penin-
sula [35]. Spain is home to areas of high terrestrial bio-
diversity [36] as well as hotspots of valuable agricultural
and pasture landscapes [37]. The country ranks second in
the EU in terms of harvested area (around 25,000,000 ha)
and agricultural production (25 billion EUR) [38], and it
contains the largest proportion (33%) of all permanent
grasslands in the EU [39, 40]. Spain contains the second
largest livestock population in the EU [24], and the ani-
mals are managed under a range of systems. The country
ranks first in the EU for the number of pigs and third for
the number of bovine animals, and it is the second larg-
est producer of pigs (4.6 million tonnes) and poultry (1.7
million tonnes) [38].

Methods

Major crop and wild plant species on agricultural and pasture lands where antibiotics are used to treat livestock were identified in two steps. First, crop land and pasture scenarios receiving manure from intensively reared or grazing animals were identified and mapped. Second, data on crop species and wild plant species present in those scenarios were analysed to determine richness, diversity and evenness in the case of wild species, or number of cultivated hectares in the case of crop species. These steps are detailed in the subsequent paragraphs. All analyses were conducted using ArcMap 10.7 [41] and R 3.6.3 [42].

Definition of scenarios

We considered the crop land and pasture scenarios
described by the “Guideline on Environmental Impact
Assessment for Veterinary Medicinal Products” [43].
Crop land scenarios were defined as crop fields that lie
adjacent to intensive animal facilities and that receive
manure from the intensively reared animals. Pasture sce-
narios were defined as lands covered with grass or herb-
age and grazed by livestock. In the crop land scenario,
we considered crops and wild species present in adjacent
habitats of crop fields, such as field margins; in the pasture scenario, we considered only wild species because grazing animals consume forage, which is incompatible with crop production.

## Mapping of agriculture and pasture scenarios

The two scenarios were mapped by combining georeferenced layers on livestock density and land cover. To build density layers of intensively reared or grazing animals, georeferenced point data on cattle, pigs, and chickens were obtained from the Spanish Register of Livestock Holdings (REGA database, General Directorate of Livestock, Spanish Ministry of Agriculture, Fisheries and Food). This database includes data on whether the animals are raised indoors or outdoors. We assumed that “indoors” equated to “intensively reared”, while “outside” equated to “grazing”, except in the case of chickens, all of which we classified as intensively reared because chickens are typically grown in fenced yards and manure surplus is applied to agricultural fields [44]. In this way, we built five kernel density layers: intensively reared cattle, grazing cattle, intensively reared pigs, grazing pigs and intensively reared chickens.

We extracted population values from the livestock census, and we applied Silverman’s Rule of Thumb [45] to define the search radius (in km) of each kernel density layer (Additional file 1: Table S1):

$$ \text{Search radius} = 0.79 * \min \left( \text{SD}, \sqrt{\frac{1}{\ln(2)} * D_m} * n^{-2} \right), $$

where SD is the standard distance; $D_m$, the median of the distances; and n, the total census.

We assumed that the probability that manure was applied to a given area was inversely proportional to the distance from an intensive animal facility, whose locations we extracted from the REGA database [46]. This reflects that farmers typically spread manure on their own land or on nearby land to avoid the costs and environmental disturbance (mainly outdoor smells) of transporting it farther away [47–49]. Similarly, we considered that the probability of grazing was inversely proportional to the distance from a primary animal location. In this way, each kernel animal density layer was categorised as having a low, medium or high manure load, with the three categories defined based on tertiles after exclusion of 0 values.

Layers of intensive crop and grassland cover were obtained from the Spanish Land Cover/Land Use Information System (SIOSE in Spanish; CNIG [50]), after reclassifying the Inspire categories (CODIIGE). SIOSE is a polygon-based spatial dataset of land cover in the country at a scale of 1:25,000, and it is the basis for Spanish

CORINE land cover data [51]. The dataset offers a minimum mapping unit of 2 ha for agricultural land, forests and natural areas. Land covers corresponding to crops were selected according to Piñeiro et al. [52] and the Spanish Ministry of Agriculture Food Security and Environment [53]. Land covers corresponding to grasslands were selected according to Ferrer et al. ([54]; Additional file 1: Table S2).

The livestock density layers were combined with the land cover layers using the mask tool in ArcGIS [41] in order to generate the crop land and pasture scenarios. During this process, intensively reared animal layers were combined with the crop layer, whereas grazing animal layers were combined with the grassland layer. We constructed separate maps of the crop land scenario for pigs, cattle and chickens; as well as separate maps of the pasture scenario for pigs and cattle. Each map classified areas by manure load. Altogether, we considered 15 “subscenarios” defined by scenario (crop land or pasture), animal species (pigs, cattle, chickens) and manure load category (low, medium, high) (Table 1).

## Identification of crop species present

Data on crop species were obtained from the annual crop database of the Spanish Ministry of Agriculture, Fisheries and Food (General Subdirectorate for Analysis, Coordination and Statistics), which comprises data from all Spanish provinces across three databases covering general land use, herbaceous crops or woody crops. For each province, crops statistics are recorded in hectares at the municipality level. For the present study, data from 2015 were extracted from all but eight provinces, for which

<table><caption>**Table 1** Subscenarios in the area of study, defined by scenario, animal species and manure load</caption><thead><tr><th>Scenario</th><th>Animal species</th><th>Manure load</th><th>Subscenario</th></tr></thead><tbody><tr><td rowspan="9">Crop land</td><td rowspan="3">Pig</td><td>High</td><td>Crop_Pig_H</td></tr><tr><td>Medium</td><td>Crop_Pig_M</td></tr><tr><td>Low</td><td>Crop_Pig_L</td></tr><tr><td rowspan="3">Cattle</td><td>High</td><td>Crop_Cattle_H</td></tr><tr><td>Medium</td><td>Crop_Cattle_M</td></tr><tr><td>Low</td><td>Crop_Cattle_L</td></tr><tr><td rowspan="3">Chicken</td><td>High</td><td>Crop_Chicken_H</td></tr><tr><td>Medium</td><td>Crop_Chicken_M</td></tr><tr><td>Low</td><td>Crop_Chicken_L</td></tr><tr><td rowspan="9">Pasture</td><td rowspan="3">Pig</td><td>High</td><td>Past_Pig_H</td></tr><tr><td>Medium</td><td>Past_Pig_M</td></tr><tr><td>Low</td><td>Past_Pig_L</td></tr><tr><td rowspan="3">Cattle</td><td>High</td><td>Past_Cattle_H</td></tr><tr><td>Medium</td><td>Past_Cattle_M</td></tr><tr><td>Low</td><td>Past_Cattle_L</td></tr></tbody></table>
data were unavailable from that year. For these eight
cases, municipality values were inferred based on the
most recent available municipality values as well as pro-
vincial values from 2015.

In this way, we generated a “crop database” integrating
general land use, herbaceous crops and woody crops in
all municipalities of Spain. This database contained 166
crops from 39 families and altogether at least 150 differ-
ent taxa.

After overlaying the geographic information available,
each municipality of the crop database was assigned to
1–5 of the 15 subscenarios (see Table 1). Within each
scenario and animal species, a given municipality could
belong to only a single manure load category, so it was
assigned to the category showing the largest area in the
municipality. Then, for the nine crop land subscenarios,
we calculated the mean percentage of total crop area cov-
ered by each crop and plant family, as well as the standard
deviation and coefficient of variation in that percentage.
Differences in crop percentages among the nine crop
land subscenarios were explored using linear mixed-
effects (LME) models [55]. Crop types were considered
as random factors in order to control for differences due
to intrinsic characteristics of each crop category. Dif-
ferences found to be significant with 95% confidence
were further assessed using Tukey’s post hoc test in the
emmeans package in R [56].

Identification of wild species present

Data on wild plant population occurrences were obtained
from the Global Biodiversity Information Facility (GBIF)
database [57]. To be included, occurrence records had to
have been deposited after 1980 and had to contain geo-
graphic coordinates to a precision of at least two deci-
mal places in both latitude and longitude. Only non-crop
species listed in Annex 2 of the OECD 208 method were
considered as targeted species in this study. The 52 origi-
nal taxa in the OECD method became 51 after species
names were harmonised using the Germplasm Resources
Information Network Taxonomy [58].

Georeferencing data on wild plant populations were
screened for intraspecific spatial duplicates, defined as
species occurrences less than 1 km apart [59, 60]. Only
one of such duplicate occurrences was retained in the
dataset. The cleaned set of population occurrences
included 69,883 records and was intersected with every
map of the crop land scenario (pigs, cattle and chickens)
and pasture scenario (pigs and cattle) in order to derive
the number of species, Simpson diversity [61], and Pie-
lou's evenness [62]. For each subscenario within the crop
land and pasture scenarios, we calculated percentages
of occurrence of each wild targeted species with respect
to the total occurrence of all the targeted species in that

subscenario. Differences in richness, diversity and even-
ness across subscenarios were explored using LME mod-
els [55]. Wild species were considered as random factors
in order to control for differences in the incidence values
due to intrinsic features of each species. When the effects
were significant with 95% confidence, a post hoc Tukey
HSD test was performed using the emmeans package in
R [56].

Finally, crop land and pasture scenarios were overlapped with a geographical information system layer that included both the Spanish Sites of Community Importance network (belonging to the Natura 2000 network; [63]) and the Biosphere Reserves in Spain [64]. The objective was to estimate the area of these protected networks that was subjected to some type of livestock pressure and, therefore, exposed to antibiotics.

Results

Mapping of crop land and pasture scenarios

Our analysis indicated that forest and shrublands cov-
ered 38.89% of the total surface in Spain; crops, 36.69%;
and permanent grasslands, 16.71% (Table 2). In the crop
land scenario, 65.10% of the total land was arable, com-
prising crops (53.43%) and fallow lands (11.67%), while
11.25% of the total land was natural grassland. In the pas-
ture scenario, 21.43% of the total land was arable (16.73%
crops and 4.70% fallow lands), while 21.83% was natural
grassland. An estimated 63.1–78.1% of total agricultural
land area and 22.4–97.5% of total pasture land area could
receive manure from livestock farms (Additional file 1:
Table S3).

The pasture scenario showed the highest spatial coverage for cattle and the lowest coverage for pigs, and the two species were also associated with, respectively, the highest and lowest percentages of surface under high manure load (Additional file 1: Table S3). These subscenarios concentrated in the western part of the country (Fig. 1b, d).

In contrast, the crop land scenario extended over flat
areas throughout the country (Fig. 1a, c, e). The land sur-
face under high manure load was twice as large for chick-
ens as for cattle and pigs (Additional file 1: Table S3).

Identification of crop species

Mean percentages of total crop area covered by each crop
family did not differ significantly across the three live-
stock species (F=0.58; p=0.56) or three manure loads
(F=0.006; p=0.93). Therefore, the mean percentages for
each crop family, as well as the associated standard devia-
tions and coefficients of variation, were averaged across
all crop land subscenarios (Table 3). Herbaceous fami-
lies covered 71.05% of crop land, with Poaceae the most
<table><caption>**Table 2** Main land cover statistics in crop land and pasture scenarios in Spain</caption><thead><tr><th rowspan="2"></th><th colspan="3">Crop land scenario</th><th colspan="3">Pasture scenario</th><th rowspan="2">Entire Spain<br>%<sup>b</sup></th></tr><tr><th>%<sup>a</sup> Mean</th><th>Std. dev.</th><th>Coeff. var.</th><th>%<sup>a</sup> Mean</th><th>Std. dev.</th><th>Coeff. var.</th></tr></thead><tbody><tr><td>Crop land</td><td>65.10</td><td>1.95</td><td>0.03</td><td>21.43</td><td>5.84</td><td>0.27</td><td>36.69</td></tr><tr><td>Crops</td><td>53.43</td><td>2.29</td><td>0.04</td><td>16.73</td><td>5.11</td><td>0.31</td><td>30.17</td></tr><tr><td>Herbaceous crops</td><td>37.89</td><td>3.63</td><td>1.06</td><td>8.38</td><td>2.86</td><td>1.56</td><td>16.98</td></tr><tr><td>Woody crops</td><td>15.55</td><td>3.95</td><td>0.25</td><td>8.35</td><td>4.05</td><td>0.49</td><td>13.18</td></tr><tr><td>Fallow lands</td><td>11.67</td><td>0.93</td><td>0.08</td><td>4.70</td><td>1.45</td><td>0.31</td><td>6.52</td></tr><tr><td>Natural grasslands</td><td>11.25</td><td>1.22</td><td>0.11</td><td>21.83</td><td>4.03</td><td>0.18</td><td>16.71</td></tr><tr><td>Mediterranean grasslands</td><td>5.38</td><td>1.36</td><td>0.25</td><td>16.78</td><td>5.51</td><td>0.33</td><td>9.60</td></tr><tr><td>Natural meadows</td><td>1.23</td><td>1.18</td><td>0.95</td><td>2.54</td><td>2.54</td><td>1.00</td><td>2.17</td></tr><tr><td>Grazed uncultivated old agricultural land</td><td>4.64</td><td>0.63</td><td>0.14</td><td>2.52</td><td>2.72</td><td>1.08</td><td>4.95</td></tr><tr><td>Forests and shrublands</td><td>15.69</td><td>1.94</td><td>0.12</td><td>50.39</td><td>2.64</td><td>0.05</td><td>38.89</td></tr><tr><td>Neither natural nor agriculture (rest of the area)</td><td>7.96</td><td>0.81</td><td>0.10</td><td>6.34</td><td>0.54</td><td>0.09</td><td>7.71</td></tr></tbody></table>

abundant family (52.60%), while woody families covered 28.94%.

Most herbaceous families in the crop land scenario matched those listed in the OECD 208 method (Additional file 1: Table S4). Monocotyledonae were present in 74.43% of total area in the crop land scenario, and dicotyledonae in the remaining 23.66%. Only around 2% of crop land was covered by families not listed in the OECD 208 method. Thirteen species covered more than 1% of crop land in the crop land scenario; the most abundant individual species were barley (*Hordeum vulgare* L.) (31.24%) and wheat (*Triticum* spp.) (24.96%) (Table 4).

## Identification of wild plant species

Forty-six of the 51 taxa listed in Annex 3 of the OECD 208 method occur in Spain; no occurrence records with coordinates were obtained from the GBIF for *Elymus canadensis* L., *Hordeum pusillum* Nutt., *Ipomoea hederacea* Jacq., *Senna obtusifolia* (L.) H. S. Irwin & Barneby, or *Sesbania exaltata* (Raf.) Rydb. Of the 46 taxa present in Spain, 42 occurred within all five combinations of scenarios and animal species. All the families containing the 42 plant species occurred in all five combinations of scenarios and animal species.

The 15 subscenarios showed similar values for richness, Simpson diversity index and Pielou index, with the exception of pig-rearing areas under medium or high manure load in the pasture scenario, which showed lower values for these parameters (Additional file 1: Table S5). Nevertheless, no significant differences were found in richness, diversity or evenness across livestock species (Richness: *F*=2.3; *p*=0.15; Simpson: *F*=2.06; *p*=0.17; Pielou: *F*=1.8; *p*=0.2) or manure loads (Richness: *F*=0.61; *p*=0.56; Simpson: *F*=0.3; *p*=0.75; Pielou:

*F*=0.22; *p*=0.8). Therefore, results were analysed further only at the level of the two scenarios.

In the crop land scenario, Poaceae, Asteraceae and Polygonaceae were the families with the largest percentages of occurrence, and the most abundant species in these families were *Echinochloa crus-galli* (L.) P. Beauv., *Xanthium spinosum* L. and *Rumex crispus* L., respectively (Table 5). In the pasture scenario, Primulaceae, Poaceae and Rubiaceae were the families with the largest percentages of occurrence, with *Bromus tectorum* L. and *Galium aparine* L. the most abundant species. Even if the same family occupied the largest percentage of crop area in both the crop land and pasture scenarios, the most abundant species from that family could differ. For example, the Poaceae species occupying the largest percentages were *Echinochloa crus-galli* and *Digitaria sanguinalis* (L.) Scop. in the crop land scenario, but *Bromus tectorum* and *Agrostis capillaris* L. in the pasture scenario. In some cases, the same species from the same family dominated both the crop land and pasture scenarios, as occurred with the Polygonaceae species *Rumex crispus* and *Persicaria maculosa* Gray (Table 5).

The coefficients of variation in the percentages of occurrence of the targeted wild species were low and ranged from 0.20 to 2.45 at the species level, and from 0.13 to 0.95 at the family level (Table 5).

We detected livestock pressure on 25% of the lands protected within the Spanish Sites of Community Importance or the Biosphere Reserves (Fig. 2). In fact, 75% of these protected areas was estimated to be exposed to antibiotics in the pasture scenario (Fig. 2). The subscenarios predicting the largest areas of exposure risk in protected lands were the cattle-rearing

<footnote><sup>a</sup> Calculated with respect to the total area of the municipalities belonging to each scenario</footnote>

<footnote><sup>b</sup> Calculated with respect to the total area of Spain</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 1** Probability of receiving manure from: a pig in crop land scenario; b pig in pasture scenario; c cattle in crop land scenario; d cattle in pasture scenario; e chicken in crop land scenario</figcaption></figure>

lands under low or medium manure load in the pasture scenario (Additional file 1: Table S6). The subscenarios predicting the smallest areas of exposure risk were pig-rearing lands under high manure load in the crop land and pasture scenarios.

# Discussion

## Crop land and pasture scenarios and subscenarios

Our maps for crop land and pasture subscenarios (Fig. 1) reveal a heterogeneous “patchwork” reflecting the diverse influences on agricultural and livestock activities in

Spain, which include bioclimatic, socioeconomic and policy factors [35, 65, 66].

Geographical areas under high manure load in our study correspond to high productive regions of crop land, pasture and meadows [67]. In the crop land scenario, areas under high manure load were located predominantly in the Iberian central high plateau and the Ebro River Basin, which feature mainly arable lands with herbaceous crops, especially cereals (Fig. 1a, c, e). We also observed areas under high manure load in southern Spain and along the Mediterranean coast that were
<table><caption>**Table 3** Minimum number of crops, minimum number of taxa and mean, standard deviation and coefficient of variation of the area covered by different crop families as a percentage of the total agricultural area in the crop land scenario</caption><thead><tr><th rowspan="2">Family</th><th rowspan="2">Crop type</th><th rowspan="2">Min. no. of crops</th><th rowspan="2">Min. no. of taxa</th><th colspan="3">Crop area (% total area)</th></tr><tr><th>Mean</th><th>Std. dev.</th><th>Coeff. var.</th></tr></thead><tbody><tr><td>Poaceae</td><td>Herbaceous (except cane)</td><td>21</td><td>19</td><td>52.60</td><td>5.85</td><td>0.11</td></tr><tr><td>Fabaceae</td><td>Herbaceous (except carob)</td><td>28</td><td>19</td><td>8.02</td><td>1.53</td><td>0.19</td></tr><tr><td>Convolvulaceae</td><td>Herbaceous</td><td>1</td><td>1</td><td>0.01</td><td>0.00</td><td>0.84</td></tr><tr><td>Cyperaceae</td><td>Herbaceous</td><td>1</td><td>1</td><td>0.00</td><td>0.00</td><td>1.56</td></tr><tr><td>Chenopodiaceae</td><td>Herbaceous</td><td>5</td><td>5</td><td>0.40</td><td>0.13</td><td>0.33</td></tr><tr><td>Malvaceae</td><td>Herbaceous</td><td>1</td><td>1</td><td>0.19</td><td>0.29</td><td>1.55</td></tr><tr><td>Linaceae</td><td>Herbaceous</td><td>2</td><td>1</td><td>0.27</td><td>0.36</td><td>1.34</td></tr><tr><td>Cannabaceae</td><td>Herbaceous</td><td>3</td><td>2</td><td>0.00</td><td>0.00</td><td>1.15</td></tr><tr><td>Asteraceae</td><td>Herbaceous</td><td>7</td><td>7</td><td>6.14</td><td>2.71</td><td>0.44</td></tr><tr><td>Brassicaceae</td><td>Herbaceous</td><td>7</td><td>6</td><td>0.84</td><td>0.36</td><td>0.43</td></tr><tr><td>Solanaceae</td><td>Herbaceous</td><td>7</td><td>6</td><td>0.76</td><td>0.19</td><td>0.25</td></tr><tr><td>Apiaceae</td><td>Herbaceous</td><td>4</td><td>3</td><td>0.08</td><td>0.02</td><td>0.30</td></tr><tr><td>Iridaceae</td><td>Herbaceous</td><td>1</td><td>1</td><td>0.00</td><td>0.00</td><td>0.43</td></tr><tr><td>Lamiaceae</td><td>Herbaceous</td><td>3</td><td>3</td><td>0.01</td><td>0.01</td><td>0.42</td></tr><tr><td>Caryophyllaceae</td><td>Herbaceous</td><td>1</td><td>1</td><td>0.00</td><td>0.00</td><td>0.51</td></tr><tr><td>Cucurbitaceae</td><td>Herbaceous</td><td>7</td><td>5</td><td>0.26</td><td>0.10</td><td>0.38</td></tr><tr><td>Asparagaceae</td><td>Herbaceous</td><td>1</td><td>1</td><td>0.08</td><td>0.03</td><td>0.37</td></tr><tr><td>Amaryllidaceae</td><td>Herbaceous</td><td>4</td><td>4</td><td>0.32</td><td>0.11</td><td>0.35</td></tr><tr><td>Agaricales</td><td>Herbaceous</td><td>1</td><td>1</td><td>0.01</td><td>0.01</td><td>1.17</td></tr><tr><td>Sown meadows</td><td>Herbaceous</td><td>1</td><td>2</td><td>0.74</td><td>0.81</td><td>1.10</td></tr><tr><td>Groups of various herbaceous spp./families</td><td>Herbaceous</td><td>13</td><td>13</td><td>0.32</td><td>0.07</td><td>0.21</td></tr><tr><td>Rutaceae</td><td>Woody</td><td>6</td><td>6</td><td>1.86</td><td>0.83</td><td>0.44</td></tr><tr><td>Rosaceae</td><td>Woody (except strawberry and rose)</td><td>15</td><td>15</td><td>6.32</td><td>4.41</td><td>0.70</td></tr><tr><td>Moraceae</td><td>Woody</td><td>1</td><td>1</td><td>0.06</td><td>0.03</td><td>0.47</td></tr><tr><td>Annonaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.00</td><td>0.01</td><td>1.66</td></tr><tr><td>Punicaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.02</td><td>0.02</td><td>0.66</td></tr><tr><td>Lauraceae</td><td>Woody</td><td>1</td><td>1</td><td>0.04</td><td>0.06</td><td>1.36</td></tr><tr><td>Musaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.01</td><td>0.01</td><td>1.55</td></tr><tr><td>Arecaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.00</td><td>0.01</td><td>2.37</td></tr><tr><td>Cactaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.00</td><td>0.00</td><td>0.53</td></tr><tr><td>Juglandaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.06</td><td>0.04</td><td>0.66</td></tr><tr><td>Betulaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.09</td><td>0.11</td><td>1.22</td></tr><tr><td>Vitaceae</td><td>Woody</td><td>1</td><td>1</td><td>5.78</td><td>2.34</td><td>0.41</td></tr><tr><td>Oleaceae</td><td>Woody</td><td>1</td><td>1</td><td>14.26</td><td>5.79</td><td>0.41</td></tr><tr><td>Capparaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.00</td><td>0.00</td><td>1.34</td></tr><tr><td>Rubiaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.00</td><td>0.00</td><td>3.00</td></tr><tr><td>Salicaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.00</td><td>0.00</td><td>1.45</td></tr><tr><td>Anacardiaceae</td><td>Woody</td><td>1</td><td>1</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Groups of various woody spp./families</td><td>Woody</td><td>11</td><td>12</td><td>0.44</td><td>0.12</td><td>0.28</td></tr><tr><td>Total</td><td></td><td>166</td><td>150</td><td>100</td><td></td><td></td></tr></tbody></table>

associated with rearing of chickens (Fig. 1e). The wider distribution of chickens than of cattle or pigs may reflect the stronger economic constraint on chicken farms to be closer to markets, rather than the influence of climatic or other environmental factors [68].

In the cattle pasture scenario, geographical areas under high manure load were present in the humid northern provinces, especially along the north and central mountain ranges, which are characterised by rich pastures and meadows. Such areas lay also in the west and southwest
<table><caption>**Table 4** Ranking of herbaceous crops based on the percentage of total herbaceous crop area covered by each crop in the crop land scenario and in Spain</caption><thead><tr><th rowspan="2">Family</th><th rowspan="2">Crop</th><th colspan="4">Crop land scenario</th><th colspan="3">Spain</th></tr><tr><th>Position</th><th>% Mean<sup>a</sup></th><th>Std. dev.</th><th>Coeff. var.</th><th>Position</th><th>Area (Ha)</th><th>Area (%)</th></tr></thead><tbody><tr><td>Poaceae</td><td>Barley (Hordeum vulgare L.)</td><td>1</td><td>31.24</td><td>3.33</td><td>0.11</td><td>1</td><td>2,609,699</td><td>28.28</td></tr><tr><td>Poaceae</td><td>Wheat (Triticum sp.)</td><td>2</td><td>24.96</td><td>2.65</td><td>0.11</td><td>2</td><td>2,179,058</td><td>23.61</td></tr><tr><td>Asteraceae</td><td>Sunflower (Helianthus annuus L.)</td><td>3</td><td>8.32</td><td>3.81</td><td>0.46</td><td>3</td><td>742,258</td><td>8.04</td></tr><tr><td>Poaceae</td><td>Corn (Zea mays L.; non-forage corn)</td><td>4</td><td>5.44</td><td>3.08</td><td>0.57</td><td>5</td><td>374,525</td><td>4.06</td></tr><tr><td>Poaceae</td><td>Oat (Avena sativa L.)</td><td>5</td><td>4.19</td><td>0.79</td><td>0.19</td><td>4</td><td>484,454</td><td>5.25</td></tr><tr><td>Fabaceae</td><td>Alfalfa (Medicago sativa L.)</td><td>6</td><td>3.72</td><td>2.17</td><td>0.58</td><td>7</td><td>257,059</td><td>2.79</td></tr><tr><td>Poaceae</td><td>Triticosecale (Triticale)</td><td>7</td><td>1.84</td><td>0.42</td><td>0.23</td><td>9</td><td>176,589</td><td>1.91</td></tr><tr><td>Fabaceae</td><td>Dried pea (Pisum sativum L.)</td><td>8</td><td>1.68</td><td>0.43</td><td>0.26</td><td>10</td><td>162,261</td><td>1.76</td></tr><tr><td>Poaceae</td><td>Rye (Secale cereale L.)</td><td>9</td><td>1.52</td><td>0.45</td><td>0.29</td><td>11</td><td>146,946</td><td>1.59</td></tr><tr><td>Fabaceae</td><td>Vetch (Vicia sativa L.)</td><td>10</td><td>1.48</td><td>0.51</td><td>0.35</td><td>15</td><td>104,400</td><td>1.13</td></tr><tr><td>Poaceae</td><td>Winter cereals for fodder</td><td>11</td><td>1.45</td><td>0.35</td><td>0.24</td><td>8</td><td>184,800</td><td>2.00</td></tr><tr><td>Fabaceae</td><td>Forage vetch (Vicia sativa L.)</td><td>12</td><td>1.32</td><td>0.60</td><td>0.45</td><td>12</td><td>120,187</td><td>1.30</td></tr><tr><td>Poaceae</td><td>Rice (Oryza sativa L.)</td><td>13</td><td>1.07</td><td>0.80</td><td>0.75</td><td>13</td><td>109,302</td><td>1.18</td></tr><tr><td>Various</td><td>Sown meadows</td><td>14</td><td>1.02</td><td>1.03</td><td>1.01</td><td>6</td><td>284,360</td><td>3.08</td></tr><tr><td></td><td>% Total of crops above 1%</td><td></td><td>89.24</td><td></td><td></td><td></td><td></td><td>85.98</td></tr></tbody></table>

of the Peninsula, where pasture lands known as “dehesas” are abundant [40]. Dehesas, an agroforestry system that harmoniously combines pastures and trees, are one of the most ecologically valuable and economically important rural landscapes on the Iberian Peninsula [67]. The distribution of dehesas coincides exactly with that of pig-rearing pastures in our study (Fig. 1b), even though cattle also graze on dehesas [69].

The crop land scenario showed extensive surface devoted to arable land, mainly for herbaceous crops such as barley and wheat, and to a lesser extent for woody crops, mainly olive, vineyards and fruit trees. In contrast, more than two thirds of the land surface in the pasture scenario was woody vegetation (forest and shrublands) and natural grass. Natural grasslands, which include meadows, rough grassland and rough grazing rangeland, are dominated by herbaceous species and are the main food source for extensively managed livestock in Spain [40]. Shrublands and open forests also play an essential role in livestock farming when green grass is in short supply [40].

### Monocotyledonous crop species in the crop land scenario

Plant toxicity assays are designed to be rapid, simple and reproducible, which helps explain why the OECD 208 method includes only herbaceous, not woody, species as sentinels for assessing risk of environmental effects due to antibiotics given to livestock. Indeed, herbaceous crop plants are the focus of efforts to fertilise arable lands with manure. In England and Wales, manure is applied to

arable crops of cereals (wheat, barley and maize), sugar beet (Saccharum officinarum L.) and potatoes (Solanum tuberosum L.), as well as to land for grazing and silage [48, 49]. In France, 36.5% of total manure used for fertiliser is spread onto grassland, 39.6% onto land for maize, 12.9% onto land for cereals and 7.9% onto land for oilseed (Brassica napus L.) or protein crops [47].

We found that in Spain, the Monocotyledonae group and concretely the Poaceae family dominated herbaceous crop species in the crop land scenario (Table 4). This reflects that cereals cover more land in Spain than any other crop, occupying 71% of the herbaceous crop area. This broad distribution reflects, in part, the plants' adaptability to different soils and climatic conditions. At the national level, cereals account for 7.1% of agrarian production and 12.3% of vegetable production [70]. We found that barley, wheat and corn occupied the top positions in the crop land scenario (Table 4), consistent with their status as the main cereals produced in Spain [70]. The situation is likely to be similar at the level of the entire EU, which is the largest wheat and barley producer worldwide and which accounts for 6% of global corn production [70]. Wheat, barley and corn are included in Annex 2 of the OECD method.

The cereals oat (Avena sativa L.), triticale (Triticale), rye (Secale cereale L.) and rice (Oryza sativa L.) are also cultivated in Spain and all were identified in the crop land scenario, but each of them accounted for less than 5% of the herbaceous crop area. A similar pattern occurs at the EU level, where rye accounts for 2.0%

<footnote><sup>a</sup> Only crops with percentages above 1% are shown</footnote>
<table><caption>**Table 5** Mean, standard deviation and coefficient of variation of the percentage of presence of wild targeted species in crop land and pasture scenarios</caption><thead><tr><th rowspan="2">Family</th><th rowspan="2">Species</th><th colspan="2">Crop land scenario</th><th colspan="2">Pasture scenario</th></tr><tr><th>Mean ± std. dev.</th><th>Coeff. var.</th><th>Mean ± std. dev.</th><th>Coeff. var.</th></tr></thead><tbody><tr><td rowspan="2">Apiaceae</td><td>Total</td><td>0.44 ± 0.11</td><td>0.26</td><td>0.54 ± 0.40</td><td>0.75</td></tr><tr><td>Torilis japonica (Houtt.) DC.</td><td>0.44 ± 0.11</td><td>0.26</td><td>0.54 ± 0.40</td><td>0.75</td></tr><tr><td rowspan="10">Asteraceae</td><td>Total</td><td>11.25 ± 1.19</td><td>0.11</td><td>9.35 ± 2.43</td><td>0.26</td></tr><tr><td>Bellis perennis L.</td><td>2.59 ± 0.19</td><td>0.07</td><td>4.05 ± 0.80</td><td>0.20</td></tr><tr><td>Centaurea cyanus L.</td><td>1.73 ± 0.43</td><td>0.25</td><td>0.83 ± 0.53</td><td>0.64</td></tr><tr><td>Centaurea nigra L.</td><td>1.01 ± 0.55</td><td>0.55</td><td>1.49 ± 1.44</td><td>0.97</td></tr><tr><td>Inula helenium L.</td><td>0.14 ± 0.06</td><td>0.45</td><td>0.08 ± 0.09</td><td>1.12</td></tr><tr><td>Leontodon hispidus L.</td><td>0.80 ± 0.18</td><td>0.23</td><td>1.08 ± 0.91</td><td>0.85</td></tr><tr><td>Rudbeckia hirta L.<sup>a</sup></td><td></td><td></td><td>0.00 ± 0.00</td><td>1.60</td></tr><tr><td>Solidago canadensis L.</td><td>0.12 ± 0.12</td><td>0.94</td><td>0.06 ± 0.06</td><td>1.13</td></tr><tr><td>Xanthium spinosum L.</td><td>3.41 ± 0.77</td><td>0.23</td><td>1.26 ± 0.61</td><td>0.48</td></tr><tr><td>Xanthium strumarium L.</td><td>1.45 ± 0.38</td><td>0.27</td><td>0.50 ± 0.20</td><td>0.40</td></tr><tr><td rowspan="2">Brassicaceae</td><td>Total</td><td>0.74 ± 0.37</td><td>0.50</td><td>1.06 ± 1.01</td><td>0.95</td></tr><tr><td>Cardamine pratensis L.</td><td>0.74 ± 0.37</td><td>0.50</td><td>1.06 ± 1.01</td><td>0.95</td></tr><tr><td rowspan="2">Caryophyllaceae</td><td>Total</td><td>0.44 ± 0.28</td><td>0.64</td><td>0.58 ± 0.42</td><td>0.72</td></tr><tr><td>Silene flos-cuculi (L.) Greuter & Bourdet</td><td>0.44 ± 0.28</td><td>0.64</td><td>0.58 ± 0.42</td><td>0.72</td></tr><tr><td rowspan="2">Chenopodiaceae</td><td>Total</td><td>6.19 ± 0.66</td><td>0.11</td><td>2.65 ± 1.28</td><td>0.48</td></tr><tr><td>Chenopodium album L.</td><td>6.19 ± 0.66</td><td>0.11</td><td>2.65 ± 1.28</td><td>0.48</td></tr><tr><td rowspan="2">Cyperaceae</td><td>Total</td><td>1.59 ± 0.53</td><td>0.33</td><td>0.47 ± 0.33</td><td>0.71</td></tr><tr><td>Cyperus rotundus L.</td><td>1.59 ± 0.53</td><td>0.33</td><td>0.47 ± 0.33</td><td>0.71</td></tr><tr><td rowspan="3">Fabaceae</td><td>Total</td><td>9.06 ± 0.73</td><td>0.08</td><td>8.64 ± 4.38</td><td>0.51</td></tr><tr><td>Lotus corniculatus L.</td><td>4.32 ± 0.43</td><td>0.10</td><td>4.00 ± 2.69</td><td>0.67</td></tr><tr><td>Trifolium pratense L.</td><td>4.75 ± 0.33</td><td>0.07</td><td>4.64 ± 1.72</td><td>0.37</td></tr><tr><td rowspan="2">Hypericaceae</td><td>Total</td><td>7.08 ± 1.38</td><td>0.20</td><td>5.81 ± 1.94</td><td>0.33</td></tr><tr><td>Hypericum perforatum L.</td><td>7.08 ± 1.38</td><td>0.20</td><td>5.81 ± 1.94</td><td>0.33</td></tr><tr><td rowspan="6">Lamiaceae</td><td>Total</td><td>5.67 ± 0.91</td><td>0.16</td><td>6.97 ± 3.25</td><td>0.47</td></tr><tr><td>Betonica officinalis L.</td><td>1.98 ± 0.54</td><td>0.27</td><td>2.71 ± 1.60</td><td>0.59</td></tr><tr><td>Leonurus cardiaca L.</td><td>0.02 ± 0.02</td><td>0.76</td><td>0.02 ± 0.02</td><td>1.13</td></tr><tr><td>Mentha spicata L.</td><td>0.48 ± 0.21</td><td>0.43</td><td>0.39 ± 0.08</td><td>0.22</td></tr><tr><td>Nepeta cataria L.</td><td>0.49 ± 0.15</td><td>0.31</td><td>0.31 ± 0.13</td><td>0.43</td></tr><tr><td>Prunella vulgaris L.</td><td>2.70 ± 0.35</td><td>0.13</td><td>3.54 ± 1.70</td><td>0.48</td></tr><tr><td rowspan="3">Malvaceae</td><td>Total</td><td>0.82 ± 0.59</td><td>0.71</td><td>0.15 ± 0.10</td><td>0.64</td></tr><tr><td>Abutilon theophrasti Medik.</td><td>0.7 ± 0.53</td><td>0.74</td><td>0.15 ± 0.09</td><td>0.62</td></tr><tr><td>Sida spinosa L.</td><td>0.10 ± 0.10</td><td>1.04</td><td>0.00 ± 0.01</td><td>1.62</td></tr><tr><td rowspan="2">Papaveraceae</td><td>Total</td><td>8.29 ± 1.16</td><td>0.14</td><td>6.47 ± 2.76</td><td>0.43</td></tr><tr><td>Papaver rhoeas L.</td><td>8.29 ± 1.16</td><td>0.14</td><td>6.47 ± 2.76</td><td>0.43</td></tr><tr><td rowspan="3">Plantaginaceae</td><td>Total</td><td>5.84 ± 1.34</td><td>0.23</td><td>7.23 ± 1.78</td><td>0.25</td></tr><tr><td>Digitalis purpurea L.</td><td>3.14 ± 1.33</td><td>0.42</td><td>5.55 ± 1.84</td><td>0.33</td></tr><tr><td>Veronica persica Poir.</td><td>2.70 ± 0.64</td><td>0.24</td><td>1.67 ± 0.60</td><td>0.36</td></tr></tbody></table>
<table><caption>**Table 5** (continued)</caption><thead><tr><th rowspan="2">Family</th><th rowspan="2">Species</th><th colspan="2">Crop land scenario</th><th colspan="2">Pasture scenario</th></tr><tr><th>Mean ± std. dev.</th><th>Coeff. var.</th><th>Mean ± std. dev.</th><th>Coeff. var.</th></tr></thead><tbody><tr><td rowspan="10">Poaceae</td><td>Total</td><td>12.59 ± 1.38</td><td>0.11</td><td>12.84 ± 2.58</td><td>0.20</td></tr><tr><td>Agrostis capillaris L.</td><td>1.59 ± 0.49</td><td>0.31</td><td>2.46 ± 2.05</td><td>0.83</td></tr><tr><td>Alopecurus myosuroides Huds.</td><td>1.28 ± 0.21</td><td>0.17</td><td>0.39 ± 0.27</td><td>0.71</td></tr><tr><td>Avena fatua L.</td><td>0.87 ± 0.23</td><td>0.27</td><td>0.59 ± 0.21</td><td>0.36</td></tr><tr><td>Bromus tectorum L.</td><td>1.59 ± 0.34</td><td>0.21</td><td>3.83 ± 2.40</td><td>0.63</td></tr><tr><td>Cynosurus cristatus L.</td><td>1.23 ± 0.67</td><td>0.54</td><td>1.94 ± 1.23</td><td>0.63</td></tr><tr><td>Digitaria sanguinalis (L.) Scop.</td><td>2.30 ± 0.36</td><td>0.16</td><td>1.27 ± 0.28</td><td>0.22</td></tr><tr><td>Echinochloa crus-galli (L.) P. Beauv.</td><td>2.80 ± 0.63</td><td>0.22</td><td>1.13 ± 0.30</td><td>0.27</td></tr><tr><td>Festuca pratensis Huds.</td><td>0.27 ± 0.19</td><td>0.71</td><td>0.35 ± 0.36</td><td>1.02</td></tr><tr><td>Phleum pratense L.</td><td>0.96 ± 0.28</td><td>0.29</td><td>0.89 ± 0.68</td><td>0.77</td></tr><tr><td rowspan="7">Polygonaceae</td><td>Total</td><td>11.00 ± 1.75</td><td>0.16</td><td>8.03 ± 1.07</td><td>0.13</td></tr><tr><td>Fallopia convolvulus (L.) Á. Löve</td><td>1.54 ± 0.41</td><td>0.27</td><td>0.87 ± 0.51</td><td>0.59</td></tr><tr><td>Persicaria lapathifolia (L.) Delarbre</td><td>2.16 ± 0.30</td><td>0.14</td><td>1.30 ± 0.40</td><td>0.31</td></tr><tr><td>Persicaria maculosa Gray</td><td>2.70 ± 0.49</td><td>0.18</td><td>1.60 ± 0.35</td><td>0.22</td></tr><tr><td>Persicaria pensylvanica (L.) M. Gómez</td><td>0.01 ± 0.01</td><td>1.11</td><td>0.00 ± 0.01</td><td>2.45</td></tr><tr><td>Rumex crispus L.</td><td>4.59 ± 0.64</td><td>0.14</td><td>4.26 ± 1.26</td><td>0.30</td></tr><tr><td>Primulaceae</td><td>Total</td><td>8.55 ± 1.88</td><td>0.22</td><td>16.48 ± 11.77</td><td>0.71</td></tr><tr><td rowspan="2">Ranunculaceae</td><td>Lysimachia arvensis (L.) U. Manns & Anderb.</td><td>8.55 ± 1.88</td><td>0.22</td><td>16.48 ± 11.77</td><td>0.71</td></tr><tr><td>Total</td><td>0.99 ± 0.28</td><td>0.28</td><td>1.26 ± 0.99</td><td>0.78</td></tr><tr><td rowspan="2">Rosaceae</td><td>Ranunculus acris L.</td><td>0.99 ± 0.28</td><td>0.28</td><td>1.26 ± 0.99</td><td>0.78</td></tr><tr><td>Total</td><td>1.41 ± 0.45</td><td>0.32</td><td>2.02 ± 1.41</td><td>0.70</td></tr><tr><td rowspan="3">Rubiaceae</td><td>Geum urbanum L.</td><td>1.41 ± 0.45</td><td>0.32</td><td>2.02 ± 1.41</td><td>0.70</td></tr><tr><td>Total</td><td>7.73 ± 1.42</td><td>0.18</td><td>9.44 ± 2.71</td><td>0.29</td></tr><tr><td>Galium aparine L.</td><td>6.83 ± 1.39</td><td>0.20</td><td>8.25 ± 3.32</td><td>0.40</td></tr><tr><td></td><td>Galium mollugo L.</td><td>0.90 ± 0.29</td><td>0.33</td><td>1.19 ± 0.76</td><td>0.64</td></tr></tbody></table>

and rice for 0.4% of the total arable land [71]. All these cereal species except triticale are listed in Annex 2 of the OECD 208 method. Triticale is cultivated for winter cereal-legume forage intercrops, but legumes are necessarily more abundant in such intercrops in order to maintain feeding values [72].

The Liliaceae family was found to occupy 0.5% of the herbaceous crop area in the crop land scenario, with the most abundant crops being onion (0.25%) and garlic (0.21%). Onion is the only species of this family listed in Annex 2 of the OECD 208 method, and it is the second most cultivated fresh vegetable in the EU. However, animal manure is not suitable for fertilising onion or other horticultural crops because bacteria, parasites and viruses in manure can contaminate the food supply [73] and lead to bioaccumulation of contaminants such as veterinary antibiotics in stems and roots [74, 75]. Nevertheless, manure can be composted and the

resulting fertiliser can be considered safe for use on vegetable crops [76].

### Dicotyledonous crop species in the crop land scenario

Dicotyledonae herbaceous crop species covered approximately 25% of the crop land scenario (Additional file 1: Table S4). The families Fabaceae and Asteraceae clearly predominated (19%), occupying the top four positions by land cover: an Asteraceae species (sunflower (*Helianthus annuus* L.)), followed by three Fabaceae species of alfalfa (*Medicago sativa* L.), vetch (*Vicia sativa* L.), and dried pea (*Pisum sativum* L.) (Table 4). All these species except alfalfa are included in Annex 2 of the OECD 208 method.

Sunflower is an oilseed species, so it is used for human and animal consumption, fuel and industrial purposes, giving it high economic value [77]. Oilseed species come from different Dicotyledonae families: rape, which accounts for 59% of total oilseed production in the EU,

<footnote>ª Information about five populations was obtained from the Global Biodiversity Information Facility (GBIF) database [57]: one in the Past_Cattle_H subscenario, one in the Past_Cattle_M subscenario and three in the agricultural and pasture scenarios</footnote>
<figure><img src="image_6.png" /><figcaption>**Fig. 2** Overlap of the Sites of Community Importance (SCI) and Biosphere Reserves with crop land (a) and pasture scenarios (b). Dark coloured areas correspond to areas of crop land (orange) and pasture (green) scenarios, respectively, located inside a SCI or a Biosphere Reserve</figcaption></figure>

comes from the Brassicaceae family; sunflower, from Asteraceae; and soybean (*Glycine max* L.), from Fabaceae (EC, 2021). Of these oilseed species, only sunflower and rape have a notable presence in Spain [78]; rape occupied 0.92% of the crop land scenario, placing it at 15th position in terms of land cover.

Alfalfa and vetch are fodder crops, although vetch is also used for human consumption. Fodder crops

comprise legumes, cereals, sown meadows and other crops (Eurostat [38]). Fodder crops are important for extensively managed livestock in Spain [40]. Among the four most abundant fodder species in our analysis, alfalfa is by far the most important in Spain (Table 4) and the EU [40]. Vetch species are also frequently used in combination with a cereal such as oat or barley or with a pasture grass such as Italian ryegrass [40, 72].
Dried pea is a protein-rich crop, and such crops are a major source of amino acids for human and animal nutrition. Dried pea ranked eighth for land cover in the crop land scenario (Table 4), and it is the most widespread protein-rich crop in Spain [78]. At the EU level, pea, bean and lupine are major cultivated protein crops [79] https://ec.europa.eu/info/food-farming-fisheries/plants-and-plant-products/plant-products/cereals_en.

Among the other dicot families and crop species listed in Annex 2 of the OECD 208 method, only the Chenopodiaceae family and sugar beet species have some importance in Spanish agriculture, accounting for 0.5% of herbaceous crops (20th position). The EU is the world's leading producer of sugar beet, accounting for around 50% of global production [79]. Most sugar beet in the EU is grown in northern Europe, where the climate is more suitable for this crop [80].

## Sentinel crop species in the crop land scenario

The European Medicines Agency recommends that assessments of risk from veterinary antibiotics include plant species that are grown on agricultural land that are fertilised with manure [81]. It would also be advisable to take into account the intrinsic susceptibility of plant species to antibiotics [16], but such data are nearly absent from the literature [82]. Thus, given the currently available data, the best species for risk assessments may be those that are abundant on agricultural land and that receive manure fertiliser.

Given that Poaceae is the crop family with the largest area in the crop land scenario, it would be advisable to consider at least two monocot species from among barley, wheat or corn (Zea mays L.) in the risk assessment of antibiotics used to treat livestock species. Such assessment should also consider the dicot species of sunflower (Asteraceae) as soilseed, dried pea (Fabaceae) as a protein-rich crop, as well as alfalfa and vetch (Fabaceae) as fodder crops. It may also be helpful to consider the oilseed rape (Brassicaceae) and sugar beet (Chenopodiaceae), since both are widely cultivated in the EU.

## Wild species in crop land and pasture scenarios

The Mediterranean Basin is one of the 25 biodiversity hotspots around the globe where many endemic species are losing their habitat [83]. The only hotspots of importance in Europe are the Mediterranean Basin and the Caucasus, both of which are extremely vulnerable to climate change [84]. As a representative region of the Basin, Spain has among the greatest diversity of ecosystems, habitats and natural species in the EU, housing more

than half of the Union's species of vertebrates and vascular plants as well as 65% of its priority habitats [19].

Most pasture areas serve for grazing by livestock and/or wild ungulates, but they also provide other valuable regulation and cultural ecosystem services [67]. Spanish pasture lands contain an abundance of Poaceae species as well as numerous other families catalogued in the Habitats of Community Interest under the EU Habitats Directive (92/43/EEC) [40]. Our results highlight *Lysimachia arvensis* (L.) U. Manns & Anderb. (Primulaceae) and *Galium aparine* (Rubiaceae) as the most frequent species (Table 5). *Lysimachia arvensis* has been reported to cause cattle death in Uruguay and potentially also sheep death in Australia [85]. In contrast, cleaver (*Galium aparine*) may support plant biodiversity [86]. The following species appear in Annex 3 of the OECD method and have been described as most abundant in Spanish pasture lands [40], which may make them useful for assessing risk from veterinary antibiotics: *Agrostis capillaris* L., *Bromus tectorum* L. and *Festuca pratensis* Huds. within the Poaceae family, as well as *Trifolium pratense* L. and *Lotus corniculatus* L. within the Fabaceae family. However, in the pasture scenario, species from this genera accounted for only 2–5% of all wild species, while *Festuca* species accounted for < 1%. These species may therefore not be sufficiently representative. In other words, Annex 3 does not include most of the dominant plant species on Mediterranean pasture lands.

Arable field margins provide a wide range of ecosystem services in rural landscapes and limit the negative effects of competitive and dominant species on crop productivity [87]. Plant diversity in these habitats has declined in recent decades due to agricultural intensification [86, 88–91]. Thus, one of the main challenges of agriculture worldwide is to balance crop productivity and biodiversity maintenance.

In typical Mediterranean cereal cropping systems in the Guadalquivir river basin in southern Spain, Pallavi-cini et al. [91] identified 306 weed species, of which only 10 are catalogued in the OECD Annex 3. Among these catalogued species, the four most abundant were also among the five most frequent in our crop land scenario: *Lysimachia arvensis*, *Galium aparine*, *Papaver rhoeas* L. and *Chenopodium album* L. Similarly, in cereal fields of the Ebro basin in northeastern Spain, Cirujeda et al. [89] identified 175 weed species. Only 6 of the most abundant species (occurring in > 10% of the surveyed fields) are included in the OECD Annex 3, with *Papaver rhoeas* the most abundant. This similarity in most abundant species between our mapped scenarios and previous fieldwork
suggests that our analysis based on the GBIF data is reliable.

In addition, our results indicate the relevance of *Hypericum perforatum* L., the fourth most frequent wild species in Spain among those in Annex 3 (Table 5). This species was not inventoried by Pallavicini et al. [91] or Cirujeda et al. [89], which may indicate that this moisture-loving species does not prefer the hot, dry conditions in the Ebro and Guadalquivir river basins.

Cirujeda et al. [88, 89] identified several extremely abundant weed species in Spanish cereal fields not included in the OECD 208 method, including *Avena sterilis* L., *Convolvulus arvensis* L., *Dactylis glomerata* L., *Lolium rigidum* Gaudin and *Sonchus oleraceus* L. The role of weeds in supporting biological diversity within crop fields has been studied by Marshall [86] and Storkey [90]. From a total of 32 species identified in those studies as important for biodiversity, only five are included in Annex 3 of the OECD 208 method. All of them were present in our crop land scenario: *Chenopodium album* (Chenopodiaceae), *Galium aparine* (Rubiaceae) and *Papaver rhoeas* (Papaveraceae) accounted for 5–8% of the total occurrence records of all targeted species in this scenario, while *Persicaria maculosa* Gray and *Fallopia convolvulus* (L.) Á. Löve (Polygonaceae) accounted for 1.5–2.7% of the total occurrence records (Table 5).

## Sentinel wild species in crop land and pasture scenarios

Our analysis suggests that assessments of risk from veterinary antibiotics should take into account *Papaver rhoeas*, *Galium aparine* and *Chenopodium album* as sentinel wild species. These species are important for biodiversity and they are abundant among targeted wild species in our crop land scenario.

Our analysis of the pasture scenario suggests that risk assessments should not take into account *Lysimachia arvensis*. Even though this species belongs to the most abundant family (Primulaceae), the plant has been linked to livestock death [85]. Instead, our analysis suggests that risk assessments should include *Bromus tectorum* or *Agrostis capillaris* from the Poaceae family, as well as *Trifolium pratense* or *Lotus corniculatus* from the Fabaceae family. All these species are relatively abundant among targeted wild species and they are dominant in Mediterranean grasslands [40]. The species *Galium aparine* (Rubiaceae) could also be considered, since it was the second most frequent species in our pasture scenario and it can support biodiversity.

While all the above-mentioned species have been listed in Annex 3 of the OECD 208 method, there are other

weed species at the margins of arable fields and on pasture lands that could be useful in assessments of risk from veterinary antibiotics but are not listed in Annex 3. These species may include those identified as the most frequent in surveys of field boundaries [88, 89, 91] and pasture lands [40] and that support biodiversity or food security. For example, *Sonchus oleraceus* is one of the most frequent weed species at field boundaries in Spain [88], and it is important for biodiversity [90]. Some Poaceae species, such as *Avena sterilis*, *Dactylis glomerata*, *Hordeum murinum* and *Lolium rigidum*, are also frequent and are included on the Prioritised Spanish Checklist of Crop Wild Relatives [92], which compiles alternative crop species crucial to ensure food security.

The identification of sentinel species may be especially beneficial for biodiversity conservation within the Spanish Sites of Community Importance network and Biosphere Reserves, given that up to 75% of these protected areas may be vulnerable to phytotoxic effects from veterinary antibiotics, according to our pasture scenario. Future work to consider new “sentinel” species not currently in Annex 3 will also need to evaluate how feasible it is to develop standardised testing protocols akin to the OECD 208 method. This will depend on the commercial availability of seeds and whether they meet the conditions for laboratory assays (e.g. germination rate ≥ 70% and survival rate ≥ 90%).

## Conclusions

Based on geographical information systems mapping, we propose the inclusion of certain sentinel crop and wild species as the most appropriate sentinel plants in assessments of environmental risk from antibiotics used to treat livestock in Mediterranean Europe. We propose barley (*Hordeum vulgare* L.), wheat (*Triticum* sp.), corn (*Zea mays* L.), sunflower (*Helianthus annuus* L.), dried pea (*Pisum sativum* L.), alfalfa (*Medicago sativa* L.), vetch (*Vicia sativa* L.), oilseed rape (*Brassica napus* L.) and sugar beet (*Saccharum officinarum* L.) as sentinel crop species in the crop land scenario. As sentinel wild species, we propose *Bromus tectorum* L., *Agrostis capillaris* L., *Trifolium pratense* L., *Lotus corniculatus* L. and *Galium aparine* L. in the pasture scenario; and *Papaver rhoeas* L., *Galium aparine* and *Chenopodium album* L. in the crop land scenario. Although most of these species are already included in the OECD 208 method (with the exception of alfalfa), we also urge the consideration of several weed species common at field boundaries or on pasture lands that are not listed in the OECD 208 method but that contribute substantially to biodiversity.

The method developed for this study could be useful for identifying sentinel species for the main agroclimatic
regions of Europe different from the Mediterranean one (i.e. Continental or Atlantic) to ensure the broadest protection of plant biodiversity according to the Biodiversity Strategy for 2030 of the European Union's Green Deal.

**Abbreviations**

GBIF: Global Biodiversity Information Facility; LME: Linear mixed-effects; REGA: Spanish Register of Livestock Holdings.

**Supplementary Information**

The online version contains supplementary material available at https://doi.org/10.1186/s12302-022-00608-0.

Additional file 1. Additional tables.

**Acknowledgements**

Not applicable.

**Authors' contributions**

RMG: contributed in formal analysis, investigation, and writing of the original draft. JFM contributed in methodology, formal analysis, resources, data curation, visualisation and writing—original draft. AR contributed in methodology and formal analysis—kernel models, geostatistical procedures and mixed-effects models. He collaborated in the writing review and editing. AT is responsible for conceptualisation, provision of data and writing, review and editing. All authors read and approved the final manuscript.

**Funding**

This work was supported by the Spanish Ministry of Science and Innovation, the Spanish State Research Agency and the European Regional Development Fund (RTI208_095586_B_C21; MCI/AEI/FEDER, UE).

**Availability of data and materials**

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Data from Spanish Register of Livestock Holdings (REGA) are not available due to data privacy laws.

**Declarations**

**Ethics approval and consent to participate**

Not applicable.

**Consent for publication**

Not applicable.

**Competing interests**

The authors declare that they have no competing interests.

**Author details**

¹Spanish Plant Genetic Resources National Centre, INIA-CRF/CSIC, Alcalá de Henares, Spain. ²Forest Research Centre, INIA-CIFOR/CSIC, Madrid, Spain. ³Animal Health Research Centre, INIA-CISA/CSIC, Valdeolmos, Madrid, Spain.

Received: 4 November 2021 Accepted: 11 March 2022

Published online: 14 April 2022

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