Characteristics of included studies
A total of 644 studies were identified from the databases examined, including 206 from PubMed, 253 from Scopus, and 185 from Web of Science (Fig. 1). We were unable to identify any additional studies from Google Scholar. After a detailed assessment based on the eligibility criteria and availability of data, 34 unique studies were selected for further analysis.
The characteristics of the studies included in this systematic review are listed in Table 2. Overall, these studies were classified into two groups: those evaluating seed emergence, and those evaluating plant growth. In the first group, seed emergence was evaluated using standardised guidelines developed by international organisations such as the Environmental Protection Agency (EPA), the American Society for Testing and Materials (ASTM) and the International Seed Testing Association (ISTA). In these studies, all assays were performed in a Petri dish with filter paper, and seed growth occurred mainly in the dark in culture media other than soil. Seed emergence and seed growth (radicle, hypocotyledon, and cotyledon length; mg/L) were recorded within 14 days after the start of the assay, mostly in the first week (Fig. 2). In five studies, we found further information on plant growth after 14 days using sand as a culture medium. The second group of studies evaluated plant growth on the basis of the OECD 208 standardised test. Here all assays were performed in the soil, plant growth occurred mainly under light/dark conditions, and seed emergence and plant growth (length and fresh/dry biomass; mg/kg) were recorded at 28 days after the start of the assay (Fig. 2).
Table 2
Characteristics of studies included in the systematic review on the phytotoxic effects of antibiotics on plant species
REFERENCE | STUDY ID | ASSAY ID | METHOD | DURATION (d) | MEDIUM | LIGHT | TEMP (ºC) | No. SEEDS | No. CONCENTRATIONS | SUBSTANCE APPLICATION | REPLICATES | UNIT |
Timmerer et al., 2020 | 1 | 1 | Phytobiotest MBT | 5 | filter paper | Dark | 23 | 9 | 5–11 | Citric acid buffer | 4 | mg/L |
Pino et al., 2016 | 2 | 1 | OECD 1984 | 5 | Filter paper | Dark | 22 | 20 | 5 | Water | 3 | mg/L |
Hillis et al., 2011 | 3 | 1 | ASTM 2003 | 5–7 | Filter paper | Dark | 24 | 10 | 6 | Water | 5 | mg/L |
Tasho et al., 2020 | 4 | 1 | ISTA 1985 | 6 | Soil | Dark | 25 | 5 | 9 | Water | 3 | mg/kg |
2 | Non- standardised | 15 | Soil | Light/Dark | 25 | 5 | 9 | Manure | 3 | mg/kg |
Luo et al., 2019a | 5 | 1 | Non-standardised | 2 | Filter paper | Dark | 25 | 20 | 7 | Water | 4 | mg/L |
2 | Non- standardised | 2 | Filter paper | Dark | 25 | 160 | 7 | Water | 0 | mg/L |
Luo et al., 2019b | 6 | 1 | Non- standardised | 2–3 | Filter paper | Dark | 25 | 160 | 7 | Water | 0 | mg/L |
Rede et al., 2019 | 7 | 1 | EPA 2012 | 5 | Soil | Light/Dark | 24 | 20 | 9 | Water | 9 | mg/kg |
Litska et al., 2019 | 8 | 1 | OECD 1984 | 21 | Soil | Light/Dark | 25 | 20 | 5 | Water | 3 | mg/kg |
Parente et al., 2018 | 9 | 1 | OECD 1984 | 16 | Soil | Light/Dark | 25 | 5 | 7 | Water | 4 | mg/kg |
Elezz et al., 2019 | 10 | 1 | Non- standardised | 6 | Filter paper | Light/Dark | | 10 | 5 | Water | 4 | mg/L |
Wieczerzak et al., 2018 | 11 | 1 | Phytobiotest MBT | 3 | Cotton wool | Dark | 23 | 10 | 5 | Water | 3 | mg/L |
Menezes-Oliveira et al., 2018 | 12 | 1 | ISO 2012 | 21 | Soil | Light/Dark | 20 | 10 | 6 | Acetone | 4 | mg/kg |
Motwani and Mehta, 2018 | 13 | 1 | Non- standardised | 7 | Filter paper | Light/Dark | 25 | 30 | 6 | Water | 3 | mg/L |
Bellino et al., 2018 | 14 | 1 | Non- standardised | 10 | Filter paper | Dark | 25 | 20 | 5 | Water | 5 | mg/L |
2 | Non- standardised | 7 | Filter paper | Dark | 25 | 20 | 5 | Water | 0 | mg/L |
Litskas et al., 2018 | 15 | 1 | OECD 1984 | 21 | Soil | Light/Dark | 25 | 5 | 5 | Water | 4 | mg/kg |
Pan and Chu., 2016 | 16 | 1 | ASTM 2003 | 5–7 | Filter paper | Dark | 25 | 20 | 6 | Water | 5 | mg/L |
Orzoł and Piotrowicz-Cieślak., 2017 | 17 | 1 | Phytobiotest MBT | 7 | Filter paper | Dark | 23 | 10 | 8 | Water | 4 | mg/L |
2 | Phytobiotest MBT | 12 | Filter paper | Dark | 23 | 10 | 8 | Water | 4 | mg/L |
Rydzyński et al., 2017 | 18 | 1 | Non- standardised | 30 | Soil | Light/Dark | 19_23 | 300 | 5 | Water | 1 | mg/kg |
Riaz et al., 2017 | 19 | 1 | Non- standardised | 2 | Filter paper | Dark | 26 | | 8 | Water | 3 | mg/L |
2 | Non- standardised | 20 | Sand | Light/Dark | 25_21 | | 3 | Water | 3 | mg/L |
Minden et al., 2017 | 20 | 1 | Non-standardised | 14 | Filter paper | Dark | 24 | 100 | 3 | Water | 9 | mg/L |
2 | Non- standardised | 56 | Filter paper | Light/Dark | 24 | 10 | 3 | Water | 9 | mg/L |
Richter et al., 2016 | 21 | 1 | OECD 1984 | 28 | Soil | Light/Dark | 22 | 20 | | Acetone | 4 | mg/kg |
Eluk et al., 2016 | 22 | 1 | ASTM 2003 | 7 | Filter paper | Dark | 25 | 10 | 4 | Water | 5 | mg/L |
Ghava et al., 2015 | 23 | 1 | Non- standardised | 12 | Filter paper | Dark | 24 | 5 | 6 | Water | 3 | mg/L |
Furtula et al., 2012 | 24 | 1 | EC 2005 | 14 | Soil | Light/Dark | 24 | 10 | 8 | Water | 6 | mg/kg |
Xie et al., 2011 | 25 | 1 | Non- standardised | 3 | Water | Dark | 25 | 600 | 10 | Water | 0 | mg/L |
Wang et al., 2019 | 26 | 1 | Non- standardised | 4 | Filter paper | Dark | 25 | 10 | 5 | Water | 3 | mg/L |
Pannu et al., 2012 | 27 | 1 | Non- standardised | 10 | Soil | Light/Dark | 25 | 30 | 3 | Methanol | 4 | mg/kg |
Liu et al., 2009a | 28 | 1 | ISTA 1985 | 4–5 | Filter paper | Dark | 25 | 15–20 | 7–8 | Water | 3 | mg/L |
2 | OECD 1984 | 20 | Soil | Light/Dark | 25 | 8–10 | 7–8 | Water | 3 | mg/kg |
Liu et al., 2009b | 29 | 1 | OECD 1984 | 20 | Soil | Light/Dark | 25 | 1_8 | 7 | acetone | | mg/kg |
Jin et al., 2009 | 30 | 1 | Non- standardised | 2 | Soil | Dark | 25 | 15 | 7 | Water | 3 | mg/kg |
2 | Non- standardised | 2–5 | Soil | Dark | 25 | 15 | 7 | Water | 3 | mg/kg |
Migliore et al., 2003 | 31 | 1 | Non- standardised | 17–37 | agar | Light | 20 | 144_206 | 3 | Sodium hydroxide | ? | mg/L |
Migliore et al., 1995 | 32 | 1 | Non- standardised | 17–27 | agar | Light | 20 | 30_60 | | Water | 10 | mg/L |
Sidhu et al., 2019 | 33 | 1 | Non- standardised | 15 | Soil | Light/Dark | 25 | 2_5 | 3 | Water | 3 | mg/kg |
Hillis et al., 2008 | 34 | 1 | Non- standardised | 14 | M medium | Dark | 26 | 6 | 5 | Methanol | 6 | mg/L |
2 | Non- standardised | 21 | M medium | Dark | 26 | 6 | 5 | Methanol | 6 | mg/L |
3 | Non- standardised | 28 | M medium | Dark | 26 | 6 | 5 | Methanol | 6 | mg/L |
The first group had a larger number of studies (n = 20) and more data on toxicity, but their results did not resemble in vivo conditions. The principle of the seed emergence assay is to determine seed vigour, which can reliably predict field performance. This assay is usually applied to monitor the viability of stored seed collections, but it has also been recommended to provide information on the germination requirements of threatened species (Clemente, 2017). Seeding is a conservative process and the seed coat acts as a barrier to protect the plant embryo from the negative impacts of environmental contaminants such as pharmaceuticals (Hillis et al., 2011, Rede et al., 2019).
In contrast, the second group had fewer studies (n = 14), but was more representative of field conditions since the studies used soil as the medium. It is widely accepted that elongation and vegetative parameters are sensitive endpoints to evaluate phytotoxic effects caused by the physical interaction of roots with antibiotics and other soil contaminants (Minden et al., 2017).
Plant species and antibiotics
We collected 192 data records on plant species, antibiotics, and toxicity endpoints from studies performed in soil (mg/kg) and 281 data records from studies performed in other media (mg/L). This is one of the limitations of our study, since each of these groups accounted for < 10% of the data on PPPs that were analysed by Christl et al (2018). Furthermore, most of the data analysed in the present study was from crop species: the data from wild species accounted for < 1% of the data in each group, much less than the 60% in the analysis by Christl et al (2018). It is clear that the effects of antibiotics on plants, particularly non-crop species, have received very little attention (Minden et al, 2017).
The most frequently assayed antibiotics with endpoint data (ECx) from crop and wild species were tetracyclines (n = 247; crop = 245, wild = 2), sulphonamides (n = 169; crop = 167; wild = 2), quinolones (n = 141; crop = 135; wild = 6), macrolides (n = 106; crop = 96; wild = 10), and penicillins (n = 62; crop = 55; wild = 7). A large majority of these antibiotics correspond to the top-selling antibiotic classes within the EU, according to the most recent European Surveillance of Veterinary Antimicrobial Consumption (ESVAC) report on antibiotic sales (EMA, 2019): tetracyclines (32.6%), penicillins (28.8%), sulphonamides (9.8%) and macrolides (7.9%).
The most frequently evaluated plant families with data on crop and wild species were Poaceae (n = 230; crop = 212, wild = 18), Fabaceae (n = 97; crop = 94, wild = 3), and Brassicaceae (n = 101; crop = 98, wild = 3). There were no data on wild species of any of the other plant families. The most common crop species observed were the two cereals Oryzia sativa and Triticum aestivum (Poaceae), as well as Brassica campestris and Brassica napus (Brassicaceae), and Lupinus luteus and Phaseolus vulgaris (Fabaceae). Most of these crops are listed in Annex 2 of the OECD 208 Terrestrial Plant Test, which is the assay recommended in the “Guideline on the plant testing strategy for veterinary medicinal products” (EMA/CVMP/ERA/689041/2015). These species are common crop species in the EU, except Lupinus luteus, which is a forage crop whose cultivation has decreased greatly in recent decades. Poaceae cereals, including rice, are the main crops grown in the EU-28 (EUROSTAT, 2017) and occupy 32.3% of the total arable land. Among these species, rice (Oryzia sativa) accounts for > 3%, while wheat (Triticum aestivum) accounts for 46% of the cropland. Pulses (Fabaceae) and vegetables (Brasicaceae) are grown, respectively, on 1.2% and 1.1% of EU cropland.
The wild species analysed were Apera spicaventi, Festuca arundinacea, and Lolium perenne (Poaceae), Capsella bursa-pastoris (Brassicaceae), and Trifolium pratense (Fabaceae). They are common weed species, or species found in the margins of fields in Europe; in some cases, they are native (or cultivated) forage crops, meadow, grassland, or pasture species (Polunin, 1977, Gómez, 2008). Floral diversity in crop margins can play a relevant ecological role in the agricultural landscape by providing a niche for invertebrates and serving as an important food source for birds (Vickery et al., 2009). None of the wild species analysed in the present study, except Trifolium pratense, is listed in Annex 3 of the OECD 208 Terrestrial Plant Test.
Native species are expected to have more variation in sensitivity than crop species (Olszyk et al 2008). Based on the endpoint data (ECx) collected from the plant growth studies performed on soil (mg/kg), wild plant toxicity data fell within the range of crop plant toxicity data (Fig. 3). However, the ranges plotted for crop species were broader than those for wild species, which may reflect the lack of data on wild species.
Among crop species, medians and interquartile ranges were lower for seed emergence data than for plant growth data.
Quotient approach
Based on the quotient approach, we calculated the differences in sensitivity between crop and wild plant species (Table 3). We were unable to calculate average quotients for the data on seed emergence (mg/L), since they did not include at least three different EC50 and three different EC10 values from wild plant species. In contrast, we found differences in sensitivity when we assessed the datasets from the studies on plant growth (mg/kg). When we compared the geometric mean values for endpoints from the most sensitive species, we found that wild species were more sensitive (Q > 1), especially with respect to vegetative ecotoxicological endpoints such as biomass. However, due to the lack of data on wild species (n = 3), we could not detect reliable differences in sensitivity between crops and wild species.
Table 3
Sensitivity of crop and wild species to antibiotics based on quotients calculated from endpoint (ECx) data in studies on plant growth
Group or variable | | Seed germination | Growth (elongation) | Growth (biomass) |
| EC50 | EC10 | EC50 | EC10 | EC50 | EC10 |
CROP | n | 48 | 35 | 49 | 27 | 7 | 14 |
| RPgeo mg/kg | 94.92 | 11.79 | 126.70 | 20.79 | 18.74 | 5.47 |
| RPmin mg/kg | 6.01 | 0.058 | 6.27 | 0.22 | 0.36 | 0.26 |
WILD | n | 1 | 1 | 3 | 1 | 3 | 3 |
| RPgeo mg/kg | - | - | 107.38 | - | 5.60 | 0.42 |
| RPmin mg/kg | - | - | 0.25 | - | 0.17 | 0.01 |
QUOTIENT | Qgeo | - | - | 1.2 | - | 3.3 | 13.1 |
| Qmin | - | - | 25.1 | - | 2.1 | 26.0 |
Wild species sensitivity
Several studies comparing the effects of antibiotics on crop and wild species from the same plant family have reported that antibiotics may be equally or more harmful to wild plant species than to crop species. One study on Poaceae species reported no significant differences in sensitivity to amoxicillin and found a similar range of amoxicillin concentrations (45.44–110.94 mg/kg) in crop species (Zea mays) and wild species (Festuca arundinacea) (Listkas et al., 2018). However, the authors of that study highlighted that amoxicillin can degrade rapidly in soil, hence decreasing the risk of acute toxicity in plants. Another study reported that tylosin negatively affected emergence and growth of Fabaceae species, and that non-crop species (Trifolium pratense) were more sensitive than crop species (Phaesolus vulgaris) in terms of EC10 (7.7 vs. 9.1 mg/kg) and EC50 (23.5 vs. 107 mg/kg) (Richter et al 2016).
A study on plant species from the Poaceae and Brasicaceae families found that exposure to different antibiotics (penicillin, sulfadiazine, and tetracycline), at concentrations similar to those detected in the soil, did not adversely affect the germination rate of crop or wild species (Minden et al., 2017). Nevertheless, exposure to those antibiotics did delay germination and affected plant growth at later stages (e.g., canopy and chlorophyl production). These effects were stronger in non-crop species (Capsella bursa-pastoris; Brassicaceae and Apera spicaventi; Poaceae) than in crop species (Brassica napus; Brassicaceae and Triticum aestivum; Poaceae). The results of that study indicate that antibiotics can affect the growth of wild plant species to a larger extent than they affect the growth of crop species. This can affect the composition of plant communities at field margins, which may trigger changes in species composition and affect biodiversity in the region (Minden et al., 2017).
The hypothesis that wild species are intrinsically more sensitive to PPPs than crop species has been tested (Christl et al. 2018). After conducting a critical review of available data on wild and crop species and statistically analysing the differences in their intrinsic sensitivity to such products, those authors found no consistent differences between the two groups of plants. In fact, crop species were found to be slightly more sensitive than wild plant species. Our review used a similar approach to analyse the effects of antibiotics on crop and wild species, but it could not arrive at a clear conclusion. One major constraint was the lack of published data on wild plant species. ERAs of veterinary medicines can contain additional sources of data, but we were unable to access such data. Moreover, a majority of pharmaceutical veterinary medicine products (> 95%) are considered to have limited environmental release, resulting in low tier (Phase I) risk assessments that do not require the analysis of ecotoxicological data (Fabrega and Carapeto, 2020).
Even though we were unable to arrive at a clear conclusion, the findings of this review can contribute to the current state of knowledge concerning the environmental risk assessment of antibiotics. Further work must be conducted to gain a better understanding of the effects of toxicity on wild plants. This is especially important for preserving biodiversity and enhancing natural capital in the EU given the requirements of the European Green Deal (EC, 2019).