extfieldsofstudy: Medicine Biology

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Interspecific variations in the habitats of Reichardia tingitana (L.) Roth leading to changes in its bioactive constituents and allelopathic activity

Reichardia tingitana is an annual plant growing in different habitats of the Egyptian deserts. Little is known about variation among the habitats occupied by this species, its distribution, chemical composition, and allelopathic activity. The present study aimed to (a) assess the vegetation composition of three different habitats (Western Coast, Delta Coast, and Wadi Hagoul) of R. tingitana in Egypt, (b) determine their correlation to soil factors, (c) identify the changes in the bioactive constituents of R. tingitana in the three regions, and (d) evaluate the allelopathic activity regarding the variation in the habitat. Density and cover of all plant species associated with R. tingitana were estimated within 52 plots, representing three regions. Physical and chemical parameters of soil were analyzed in each plot. R. tingitana aboveground biomass was collected from each habitat, and the bioactive composition was analyzed using HPLC. The allelopathic effect against two weeds (Amaranthus lividius and Chenopodium murale) was assessed. The floristic composition showed the presence of 133 species belonging to 27 families. In the Delta Coast habitat of R. tingitana, Zygophyllum aegyptium and Calligonum polygonoides co-dominate, while Lycium shawii dominate the Western Coast habitat and finally the habitat of Wadi Hagoul was dominated by Haloxylon salicornicum. Soil analysis revealed little variations among habitats, especially salinity and organic matter. Fifteen compounds, mainly phenolics (60% of the total identified compounds) were identified from all R. tingitana samples. The major compounds were quercetin, naringenin, ellagic, gallic, chlorogenic, and caffeic acids. These compounds varied in diversity or quantity among different habitats. The Western Coast sample was the richest in species, followed by Delta Coast sample. Our study showed that salinity is the crucial factor that induces the production of bioactive constituents in R. tingitana, especially phenolics and flavonoids. The R. tingitana extracts significantly reduced the germination and growth of Chenopodium and Amaranthus. However, the Western Coast sample showed potent allelopathic activity, where the germination was wholly inhibited at 75 mg L−1 and 50 mg L−1, respectively. Thereby, this extract could be used as eco-friendly bioherbicide and may be integrated into weed control strategies.

Introduction
Chemical ecology is the science addressing the role of chemical interactions between organisms and their environment. It includes signaling processes and communication between individuals (Baldwin et al., 2002). For example, after herbivores attack, the plant generates volatiles that attracts carnivorous insects that kill plant enemies (Takabayashi and Dicke, 1996). Also, infected plants can produce a signal that induces the immune system of healthy members (Baldwin et al., 2006). These chemicals that are produced by plants to either inhibit or stimulate others are called allelochemicals, and the phenomenon is known as allelopathy (Rice, 1984). The production of these compounds depends on the activahttps://doi.org/10.1016/j.sjbs.2019.11.015 1319-562X/Ó 2019 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). tion of specialized genes and is often depends on environmental conditions, such as climatic and edaphic factors (Buchanan et al., 2015).
Allelopathy plays a crucial role in the dynamics of plant species in different environments. Understanding this biological phenomenon could help in the development of applications in both natural and agricultural systems (Wardle et al., 2011). Environmental factors of any habitat can directly or indirectly affect the allelopathic potential of a plant. The most critical ecological factors that influence allelopathy include temperature, water content, salinity, nutrient availability and competition stress (Elshamy et al. 2019;Meiners et al., 2012).
Asteraceae or Compositae is one of the largest families of the flowering plants and has worldwide distribution. Most of the family members are herbaceous although a significant number of shrubs, vines, or trees belong the family. In the flora of Egypt, Asteraceae is represented by 98 genera and 228 species (Boulos, 2009). Plants under this family have been widely utilized in the past and are still used today for their medicinal properties. Members of Asteraceae have been reported to possess many biological activities including antibacterial, antifungal, antidiabetic, antihelminthic, immunostimulatory, and anticancer activities (Cazella et al., 2019;Salazar et al., 2018;Sharma et al., 2018).
Only two species of Reichardia genus were recorded in the flora of Egypt, R. picroides (L.) Roth and R. tingitana (L.) Roth. The latter is a glabrous annual herb growing wildly in the coastal and inland desert of Egypt. The stem branches from the base, the plant reaches up to 40 cm with the taproot, which gives rise to the rosette of large radical leaves first, and its flowering time extends from March to May. Preliminary phytochemical examination on aboveground biomass shows the presence of phenolics, tannins, flavonoids, coumarins, volatile oils, glycosides, flavonoids, lactones, esters, large amounts of sterols and/or triterpenes (Abdel-Mogib et al., 1993;Recio et al., 1992).
Aqueous extract of R. tingitana was reported to inhibit the germination and growth of Bidens pilosa, but the specific bioactive compounds were not determined (Abd El-Gawad et al., 2015). Moreover, little is known about the allelochemicals of R. tingitana, and according to our knowledge, no study had revealed the correlation between the habitats of R. tingitana and their bioacive composition as well as alleopathic activity. Therefore, we test the hypothesis that variations in R. tingitana habitats promote changes in the chemical composition of the plant and in consequence affect its allelopathic activity. The specific objectives of this study were to (i) determine the variations in the vegetational composition and soil factors among three different habitats of R. tingitana in Egypt, (ii) identify and compare the variation in the bioactive compounds of R. tingitana aboveground biomass from these three habitats, and (iii) evaluate the differences in R. tingitana allelopathic activity among three regions.

Study areas
The study areas can be categorized into two sections: the first is the coastal desert which is represented by two regions; Western Coast and Delta Coast, while the second is the inland desert which is represented by Wadi Hagoul (Fig. 1). The Mediterranean coastal desert of Egypt can be classified into three sections: western, delta, and eastern sectors. The Western Coast extends eastwards from Al-Sallum to Abu Qir for about 550 km with a width of about 20 km, constituting the northern coast of the Western Desert of Egypt. The meteorological data (average of >10 years, from 2000 to 2013) of Sidi Abd El-Rahman station showed that mean annual temperature ranges from 14.39 to 25.50°C, while average annual rainfall was about 192.1 mm per year. The average relative humidity was up to 66.9%. The Delta Coast is characterized by the presence of three shallow lakes, receiving the main bulk of the drainage water from the Nile Delta. This sector extends from Abu Quir to Port Said with a length of about 200 km. The metrological data of Baltim station revealed that the mean annual temperature ranges from 17.3 to 24.0°C, the average annual rainfall was about 175.2 mm per year, and the mean relative humidity was approximately 57.7%.
On the other hand, Wadi Hagoul is a drainage system occupying the valley depression between Kahaliya ridge to the south and Gebel Ataqa to the north at the Northwestern part of Gulf of Suez. The area of the wadi is 345 km 3, and its main channel extends for 35 km, where it receives water through the running channels from both sides. The wadi extends in a southeast direction toward the Gulf of Suez, crossing limestone beds of the Miocene (Zahran and Willis, 2009). Wadi Hagoul exists in the Eastern Desert, which is characterized by the arid climate. According to the meteorological data of the nearest station (Suez city), the climate is arid and dry, where the mean annual air temperature ranges from 10 to 38°C. Rainfall is scarce with 34.3 mm as yearly average, while the relative humidity is 54.4%.

Vegetation analysis
Fifty-two plots were selected to represent the three habitats (Western Coast, Delta Coast, and Wadi Hagoul) of R. tingitana. The area of the plot was 100 m 2 , estimated according to the minimal area. Within each plot, the density and cover of all species associated with R. tingitana were determined (Canfield, 1941;Shukla and Chandel, 1989). The importance value out of 200 was calculated for each species based on the relative density and cover in each plot. The taxonomic nomenclature, identification, and chorotype of plant species were assessed according to Boulos (1999Boulos ( -2005 and Tackholm (1974). However, life forms were identified according to the scheme of Raunkiaer (1937).

Soil analysis
In each plot, composite soil samples (20 cm depth) were collected, packed in polyethylene bags and returned to the laboratory. The samples were dried in the air at room temperature (25°C ± 3), sieved through 2 mm sieve to remove any contaminants, and packed in bags till further physical and chemical analyses. Soil particle size, water holding capacity (WHC), soil porosity, organic carbon, and sulphate were determined according to Piper (1947). Chlorides and calcium carbonate contents were determined according to Jackson (1962). Soil pH and electrical conductivity (EC) were measured in water suspension (1:2.5) as described by Jackson (1962). Carbonates and bicarbonates were determined by titration using 0.1 N HCl (Pierce et al., 1958). The extractable cations Na + and K + were determined by flame photometry (PHF 80B Biologie Spectrophotometer), while Ca +2 and Mg +2 were estimated using atomic absorption spectrometer (A Perkin-Elmer, Model 2380.USA) according to Allen et al. (1974).

Plant materials preparation and extraction
The above ground biomass of R. tingitana from five plants was collected from each habitat in a plastic bag and mixed as composite sample. To avoid bias, all the three habitats were sampled at the flowering stage in March and plant individuals were chosen to be of similar size. The samples were dried in room temperature (25°C ± 3), ground and packed in paper bags. About 200 g of the prepared samples were mixed with one liter of aqueous methanol (70% v/v) and shaken well for 12 h. The suspensions were then cen-trifuged at 2395 g for 10 min, and evaporated till dryness under rotary evaporator. The residues were collected in a glass vial till further analyses.

HPLC analysis
The extracted samples of the three Reichardia habitats were analyzed by HPLC. The analysis was accomplished using an Agilent 1260 series. The separation was carried out using a C18 column (4. 6 mm Â 250 mm i.d., five lm). The mobile phase consisted of water (A) and 0.02% trifluoroacetic acid in acetonitrile (B) at a flow rate of 1 ml min À1 . The mobile phase was programmed consecutively in a linear gradient as follows: 0 min (80% A); 0-5 min (80% A); 5-8 min (40% A); 8-12 min (50% A); 12-14 min (80% A) and 14-16 min (80% A). The multi-wavelength detector was scrutinized at 280 nm. The injection volume was ten ll for each of the sample solutions, and the temperature of the column was sustained at 35°C.

Weed seed source and preparation
To test the allelopathic activity of the three collected Reichardia samples, we targeted two weeds; one infesting the cultivated field in the winter season (Chenopodium murale, hereafter; Chenopodium) and the second one growing in summer season (Amaranthus lividius, hereafter; Amaranthus). The seeds were collected from several healthy plants that grow in cultivated land in Manzalla city, Al-Dakahlia Governorate, Egypt (31°07 0 08.4 00 N 31°51 0 56.5 00 E). Ripe and uniform seeds with almost of the same size and color were surface-sterilized using 0.3% NaOCl, by immersion for three minutes, then rinsed by sterile distilled water three times, dried on sterilized filter paper, and kept in sterilized vials until further use (Sampietro et al., 2009).

Experimental design of bioassay
For the preparation of plant extracts, different concentrations of 2.5, 5, 10, 25, 50, 75 and 100 mg L À1 of the above-mentioned residues were achieved using dimethyl sulfoxide (DMSO). Also, control treatment was achieved by DMSO. Twenty seeds of each weed were placed in 9 cm Petri dishes lined with sterilized Whatman No. 1 filter paper. The filter paper was moistened with 5 ml of each concentration and DMSO as control, in triplicates. To avoid contamination, the Petri dishes were sealed with parafilm and incubated in the growth chamber at 24°C and 28°C for Chenopodium and Amaranthus, respectively (Sampietro et al., 2009). A total of 144 plates [8 treatments (7 concentrations + control) Â 3 samples (regions) Â 2 species of weed's seeds Â 3 replicates] were obtained. After eight days for Chenopodium and ten days for Amaranthus, the germinated seeds were counted, and the radicle lengths were measured per each plate. The inhibition of germination, and root growth was calculated, concerning control, as follows:

Data analysis
The plant species diversity indexes were calculated per each habitat of Reichardia based on the vegetation analysis data as follows: where Pi = ni/ N = proportional abundance of species, i in a habitat made up of s species, ni = the number of plots containing species i and N = S ni.
On the other hand, the data of both soil analysis and allelopathic bioassays were subjected to one-way ANOVA followed by Duncan's post hoc test at probability level 0.05 using CoStat software program (CoHort Software, Monterey, CA, USA). Also, the data derived from HPLC analysis of the three samples were subjected to an agglomerative hierarchical cluster (AHC) and principal component analysis (PCA) to construct a matrix of correlation and to recognize whether a significant difference exists between different treat- ments. AHC and PCA were performed using XLSTAT statistical computer software package, version 14 (Addinsoft, New York, NY, USA).

Vegetation composition and soil factors of the three R. tingitana habitats
The floristic analysis of the present study showed that the total number of the recorded plant species is 133 species (64 annuals, three biennials, and 66 perennials) belonging to 105 genera and related to 27 families. The most abundant families were Asteraceae comprising 24 species, followed by Poaceae (22 species), Chenopodiaceae (13 species), Fabaceae (12 species), and Brassicaceae (10 species). However, other families were represented by a few species (Appendix 1). These families were also reported as major families in most of the researches within the same phytogeographical region (Abd El-Gawad and Shehata, 2014; El-Amier and Abd El-Gawad, 2017; Shaltout et al., 1995Shaltout et al., , 2015Zahran et al., 1990;Zahran and Willis, 2009). According to Raunkiaer (1937), therophytes were the most represented life form (50.38%), followed by chamaephytes (21.80%), hemicryptophytes (12.78%), geophytes (6.77%), and nanophanerophytes (6.02%). However, helophytes, parasites, and phanerophytes were represented by one species only (Appendix 1). The prevalence of therophytes over the other life forms could be attributed to the short life cycle that enables them to resist the instability of the habitat, topography variation and biotic influence. Moreover, therophytes were estimated by 50.3% for the whole Egyptian flora compared with 58.7% for the Mediterranean region and 59.4% for Egyptian Nile region (Ḥasib, 1950).

The habitat of R. tingitana in the Delta Coast
Within the habitat of R. tingitana in the Delta Coast, 66 plant species were recorded (35 annuals, two biennials, and 29 perennials) inhabiting 17 plots that were co-dominated by the halophytic plants; Zygophyllum aegyptium and Calligonum polygonoides. This habitat has average Shannon-evenness value of 0.80 and Simpson diversity value of 0.95 (Table 1). The most important associated annual species (have relatively high importance values) linked to this habitat were Hordeum murinum, Senecio glaucus, Cakile maritima, Mesembryanthemum crystallinum, and M. nodiflorum. The soil analyses of this region showed that it was characterized by significant high levels of sand, salinity, soil pore spaces, pH, SO 4 2À , and HCO 3 -, while cations except Ca 2+ were detected in low concentration (Table 2). No bicarbonates were detected. The northern coast of the Nile Delta of Egypt is mainly characterized with halophytic vegetation as it is affected with the Mediterranean Sea as well as the three shallow lakes of this coastal area that receive the main bulk of the drainage water from the Nile Delta (El-Amier and Abd El-Gawad, 2017; Zahran and Willis, 2009). Therefore, the perennial halophytes are common in this site of study, however, there were also a significant number of annuals which corroborated to the presence of sand dunes that distributed along the shoreline of the delta (El Banna, 2004). The sand dune habitat is characterized by its rich seed bank (Yu et al., 2007). It is worth mentioning here that many studies recorded the C. maritima as a common plant in this area (Abd El-Gawad and Shehata, 2014; El-Amier and Abd El-Gawad, 2017; Zahran et al., 1990). Plants of Fabaceae family tended to persist in soil rather than those of other plant functional groups, where their seeds have strong testa (Yu et al., 2007).
Moreover, Abd El-Gawad and Shehata (2014) reported that M. crystallinum (important species in the present study) survives well in sandy dune habitats and tolerates the high content of salinity along the Nile Delta Coast. Also, H. murinum, S. glaucus, and M. nodiflorum were reported as predominant species in similar habitats.

The habitat of R. tingitana in the Western Coast
The floristic analysis of R. tingitana habitat at the Western Coast revealed the dominance of Lycium shawii within 19 plots. Also, this habitat attained the highest number of species (69 species; 38 annuals, two biennials, and 29 perennials) as well as the highest diversity, where it has average Shannon-evenness value of 0.87 and Simpson diversity value of 0.96 (Table 1). The other important associated species were S. glaucus, Bromus diandrus, Suaeda pruinosa, M. nodiflorum, Cyondon dactylon, Emex spinosa, and Malva parvifolra. The community structure of this site has some important halophytes such as S. pruinosa and M. nodiflorum as well as some common associates with halophytic vegetation such as S. glaucus, B. diandrus, C. dactylon, E. spinosa, and M. parvifolra (Table 1). This reflects the salt-affected habitat in this site where the soil composition is characterized by high EC (1.84 mS cm À1 ) as well as high content of WHC, organic carbon, EC, Cl, Na + , K + , Mg 2+ , and lowest content of SO 4 2À and HCO 3 - (Table 2). This result is in consistency with other studies related to the vegetation structure of this area (Shaltout et al., 1995(Shaltout et al., , 2015. The Western Coastal belt of the Mediterranean Sea is the wealthiest part of Egypt in its floristic composition due to its relatively high rainfall (Tackholm, 1974). However, most of these species are therophytes (annuals) that flourish during the rainy season.

The habitat of R. tingitana in Wadi Hagoul
The vegetation analysis of the inland desert region (Wadi Hagoul) revealed the dominance of perennials (63 species; 28 annuals, one biennial, and 34 perennials), where the woody bush plant, Haloxylon salicornicum dominate this habitat. In this context, the dominance of perennials is typical in the desert habitat, where these plants have well-adapted strategies against drought, while the other small plants cannot compete with them (Zahran and Willis, 2009). In addition, this habitat has an average Shannonevenness value of 0.86 and Simpson diversity value of 0.96 (Table 1). The other important associated species were desert plants including; both perennials (Launaea nudicaulis and C. dactylon) and annuals (Bassia muricata, Diplotaxis harra, Erodium laciniatum, Z. simplex, and S. glaucus) that flourish during the rainy season. The soil of the plots representing this region is characterized by high content of CaCO 3 , HCO 3 and Ca 2+ and moderate levels of pH, EC, Na + , and K + (Table 2). These results are in congruence with previous studies of the vegetation of different wadis in the Egyptian deserts (El-Sharkawi et al., 1982;Mashaly, 1996;Zahran and Willis, 2009).

Bioactive composition of the three R. tingitana samples and their correlations to soil variables
Fifteen compounds were separated and identified from aboveground biomass of R. tingitana collected from the three regions through HPLC analysis (Table 3). These compounds are mainly phenolics (60.0%), while flavonoid compounds come as second rank (26.7%). The identified compounds varied either in the diversity or quantity between the three samples of the R. tingitana. Western Coast sample was the richest sample, followed by Delta Coast sample and finally Wadi Hagoul sample.
Caffeic acid, cinnamic acid, coumaric acid, ellagic acid, gallic acid, propyl gallate, and quercetin were identified in the samples of R. tingitana in the three regions with significant variation in the quantity. However, chlorogenic acid, daidzein, ferulic acid, naringenin, and syringic acid were identified in two regions, while catechin, vanillin, and rutin were only detected in the Western Coast sample (Table 3).
The R. tingitana sample collected from Western Coast attained the highest significant values of constituents compared to the others two samples (Delta Coast and Wadi Hagoul), where quercetin, ellagic acid, gallic acid, chlorogenic acid, naringenin, and caffeic acid are the major compound in this sample. On the other hand, quercetin was detected as major compound in deltaic coast R. tingitana sample, while quercetin and ellagic acid attained the highest concentration in the sample of the Wadi Hagoul. The accumulation of active secondary metabolites could be ascribed to the influences of multiple environmental factors on the wild plant during developmental and growth periods in addition to genetic factors. Certain compounds are synthesized, and their concentration significantly increase under specific environments (Liu et al., 2015).
The environmental stress factors (light, temperature, soil water, soil fertility, and salinity) eventually lead to changes in overall phy-tochemical composition which thereby play a strategic role in the generation of bioactive compounds (Yang et al., 2018). This is considered as plant behavior that improves the adaptation and survival in response to the harsh environment as well as enhances the immune system of the plant against herbivores and pathogens (Pellissier et al., 2016).
To evaluate the correlation between the different studied habitats and their content of bioactive compounds derived from HPLC analysis, we subjected the data to PCA and AHC. The sample of R. tingitana collected from the Western Coast region was not correlated to the other two samples, which reflect the variation of the chemical composition (Fig. 2a). However, the samples of Delta Coast and Wadi Hagoul were correlated to each other. On the other hand, the application of AHC reflected the same conclusion, where we can classify the samples into two groups; one comprising the Western Coast sample and the other group contained the samples of Delta Coast and Wadi Hagoul (Fig. 2b).  The different letter within each row means values with a significant difference after Duncan's post hoc test (P 0.05). LSD 0.05 : least significant difference at a probability level of 0.05.
From the PCA analysis, it was clear that quercetin was the main compounds that showed a close correlation to the Delta Coast sample, while it showed a little bit correlation to Wadi Hagoul sample.
This mean that it is considered as an indicator compound in the studied R. tingitana samples. On the other hand, ellagic acid showed a low correlation to the Western Coast sample and did not show any relationship to either Wadi Hagoul or Delta Coast samples. The other compounds showed the same correlation to all samples.   Flavonoids play an essential role in several arrays of plant life such protection against pathogens, insect attraction, pollination, and allelopathy (Winkel-Shirley, 2001). Moreover, under biotic and abiotic stresses, reactive oxygen species (ROS) are generated in the plant cells, and in consequence, flavonoids induced in the plant to scavenge ROS (Baskar et al., 2018;El-Shora and Abd El-Gawad, 2015a, b). Therefore, the higher concentration of quercetin in the R. tingitana sample from Western Coast region could be ascribed to the high salinity, where quercetin produced as a protecting agent in plants against oxidative stress.
From the other side, caffeic acid, cinnamic acid, coumaric acid, ellagic acid, gallic acid, and propyl gallate were also reported to be induced under salinity stress (Colla et al., 2013), therefore we can say that the higher the content of phenolics in the samples from the Western Coast could be attributed to the salinity level. A compound like ellagic acid has the ability to alleviate the osmotic stress (Abu El-Soud et al., 2013), therefore the higher content of ellagic acid in the samples of R. tingitana from the Western Coast habitat could be considered as a strategy of the plant to cope with salt stress, while for the sample of Wadi Hagoul it could be like a defense against the drought. Our study showed that the salinity, organic matter, and macro-elements (Na + , K + , and Mg 2+ ) may be the crucial factors that induce the production of bioactive constituents in R. tingitana, especially the phenolic acids and flavonoids.

Allelopathic activity of the three R. tingitana samples
The allelopathic effect of various extracts of R. tingitana aboveground biomass collected from three different regions (Delta Coast, Western Coast, and Wadi Hagoul) on germination of Chenopodium and Amaranthus was illustrated in Fig. 3. In general, R. tingitana extracts from all regions significantly (p 0.05) reduced the germination of Amaranthus more than Chenopodium (Fig. 3). The seed germination of Chenopodium was completely inhibited at a concentration of 75 mg L À1 under the effect of Delta Coast and Western Coast R. tingitana extracts, while it was inhibited by 58.33% for the Wadi Hagoul R. tingitana extract at the same concentration. The IC 50 values (the concentration of a substance that is required for 50% inhibition of a specific biological function) of the germination of Chenopodium were 37.85 mg L À1 , 42.33 mg L À1 and 66.06 mg L À1 for Western Coast, Delta Coast, and Wadi Hagoul R. tingitana extracts, respectively. On the other hand, the germination of Amaranthus was completely inhibited at a concentration of 25 mg L À1 under the effect of Western Coast. While it was inhibited by 76.35% and 27.78% under treatments of Delta Coast and Wadi Hagoul R. tingitana extracts, respectively. The IC 50 values of the germination of Amaranthus were 20.65 mg L À1 , 29.99 mg L À1 and 42.16 mg L À1 for Western Coast, Delta Coast, and Wadi Hagoul R. tingitana extracts, respectively.
Various extracts from R. tingitana showed significant (p 0.05) inhibition of root growth of Chenopodium and Amaranthus after Fig. 4. Allelopathic effect of Reichardia tingitana aboveground biomass collected from the three habitats on the root growth of Chenopodium murale (above) and Amaranthus lividius (below). The different letters within each line means significant difference at p 0.05. treatment, and the degree of inhibition of plant extracts increased with increasing concentration (Fig. 4). The R. tingitana extracts from the Western Coast and Delta Coast samples expressed total inhibition of Chenopodium root growth at concentration of 75 mg L À1 , while it was inhibited by 88.45% and 71.57% for the Delta Coast and Wadi Hagoul R. tingitana extracts, respectively. The IC 50 values of the root growth of Chenopodium were 19.66 mg L À1 , 36.80 mg ml À1 and 53.27 mg L À1 for Western Coast, Delta Coast, and Wadi Hagoul R. tingitana extracts, respectively.
On the other hand, the root growth of Amaranthus was entirely inhibited at a concentration of 25 mg L À1 under the effect of Western Coast. However, the extracts of Delta Coast and Wadi Hagoul R. tingitana samples inhibited it by 76.34% and 45.23%, respectively at the same concentration. The IC 50 values of the germination of Amaranthus were 4.60 mg L À1 , 31.12 mg L À1 and 59.11 mg L À1 for Western Coast, Delta Coast, and Wadi Hagoul R. tingitana extracts, respectively.
Generally, the extracts from the R. tingitana collected from the Western Coast revealed more allelopathic effect against the tested plants than the samples from Delta Coast or Wadi Hagoul regions. This activity might be attributed to the higher concentrations of phenolics and flavonoids reported in this sample. Phenolic compounds, especially quercetin, ellagic acid, gallic acid, chlorogenic acid, caffeic acid, and naringenin have been reported to possess various biological activities. Moreover, the bioactive ingredients enable the plant to face or survive in the variable stressful conditions, and they are considered as an important part of the plant defense system against pathogenic attacks and environmental stresses (Yang et al., 2018). In addition, most of the identified compounds in this sample were reported as allelochemicals (Inderjit, 1996;Li et al., 2010). Phenolics and flavonoids were reported as defense compounds against herbivores and pathogens such as bacteria and fungi (Agrawal, 2007). Quercetin and naringenin were reported as the most often cited allelopathic flavonoids (Berhow and Vaughn (1999)). Naringenin was reported to inhibit the growth of soybean (Bido et al 2010). These bioactive compounds might play a role in the allelopathic activity of R. tingitana either singular or in a synergetic manner.

Conclusion
The vegetation analysis of the three habitats of R. tingitana within three different regions (Western Coast, Delta Coast, Wadi Hagoul) revealed a significant variation in the vegetation composition as well as soil variables, while salinity, organic matter, and macro-elements (Na + , K + , and Mg 2+ ) were the most controlling factor. Fifteen identified secondary compounds were determined using HPLC, where they are varied in the diversity and quantity between the three samples of the R. tingitana. Western Coast sample had the highest number of the bioactive compounds, followed by Delta Coast sample and finally Wadi Hagoul sample. Therefore, we can say that R. tingitana extract from the same species but from different conditions caused different allelopathic (inhibitory) effect, and this supposed to be related to the variation in the bioactive constituents. Our study showed that the salinity is the key factor that induce the production of bioactive constituents in R. tingitana, especially the phenolic acids and flavonoids as well. The R. tingitana methanol extract, especially Western Delta sample, showed potent allelopathic activity against Chenopodium and Amaranthus, where the germination was utterly inhibited at a concentration of 75 mg L À1 and 50 mg L À1 , respectively. Therefore, these extracts could be used as a green source, eco-friendly bioherbicide and to be integrated into the weed control program of weeds. However, further study is needed for characterization of the specific allelochemical(s), evaluation the modes of action, and the biosafety. Also, further study is needed to confirm the mechanism(s) of salinity to induce the bioactive compounds.


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Conversion of monoculture cropland and open grassland to agroforestry alters the abundance of soil bacteria, fungi and soil-N-cycling genes

Integration of trees in agroforestry systems can increase the system sustainability compared to monocultures. The resulting increase in system complexity is likely to affect soil-N cycling by altering soil microbial community structure and functions. Our study aimed to assess the abundance of genes encoding enzymes involved in soil-N cycling in paired monoculture and agroforestry cropland in a Phaeozem soil, and paired open grassland and agroforestry grassland in Histosol and Anthrosol soils. The soil fungi-to-bacteria ratio was greater in the tree row than in the crop or grass rows of the monoculture cropland and open grassland in all soil types, possibly due to increased input of tree residues and the absence of tillage in the Phaeozem (cropland) soil. In the Phaeozem (cropland) soil, gene abundances of amoA indicated a niche differentiation between archaeal and bacterial ammonia oxidizers that distinctly separated the influence of the tree row from the crop row and monoculture system. Abundances of nitrate (napA and narG), nitrite (nirK and nirS) and nitrous oxide reductase genes (nosZ clade I) were largely influenced by soil type rather than management system. The soil types’ effects were associated with their differences in soil organic C, total N and pH. Our findings show that in temperate regions, conversion of monoculture cropland and open grassland to agroforestry systems can alter the abundance of soil bacteria and fungi and soil-N-cycling genes, particularly genes involved in ammonium oxidation.

Introduction
Modern alley cropping systems are innovative agroforestry systems, where rows of fast growing trees are planted alternately with rows of annual crops [1]. Spatial arrangement of the tree and crop components allows ecological interactions between them, which can increase the overall efficiency of the resource use if the trees and crops are not competing for the same resources [2,3]. For example, due to deeper root growth, most tree species can access water PLOS ONE | https://doi.org/10.1371/journal.pone.0218779 June 27, 2019 1 / 19 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 and nutrients from deeper soil layers than the associated crops [3,4]. Deep-rooting trees are thus capable of providing a 'safety-net' for leached nutrients from the crop rhizosphere by deep-root nutrient uptake [5][6][7][8].
In contrast to monoculture agriculture, agroforestry can be more sustainable by conserving and improving soil quality through increasing soil organic C and nutrient availability [9,10] and its potential for C sequestration [10]. Increases in organic C stocks are mainly due to litter input and root decay of the tree rows [11]. Inclusion of N 2 -fixing trees further enhances physico-chemical as well as biological properties of soil and thus contributes to an improvement of soil fertility [12][13][14]. Most trees in temperate alley cropping systems are fast-growing trees like Salix and Populus species, suitable for biomass production.
Soil microbial community structure and function are shaped by resource availability, which in turn is controlled, among other factors, by the quantity and quality of plant litter input as well as root exudation and decay [15,16]. Thus, it is likely that trees integrated in agroforestry systems affect soil microbial communities on both structural and functional levels. For example, a recent study in three poplar-based temperate agroforestry systems found greater soil fungal C-to-bacterial C ratios in the tree rows than the crop rows [17], probably due to increased input of lignin-and suberin-rich tree residues. Soil enzymatic activities and substrate utilization patterns of soil microbial communities also indicate that microbial communities in the tree and crop rows of agroforestry systems are functionally different [11,18,19]. Furthermore, other studies have reported beneficial effects of agroforestry on the abundance and diversity of the soil microbial community [12,20,21]. A recent study investigated for the first time the soil bacterial community structure in temperate agroforestry systems using next generation sequencing [22]. They found that the integration of trees increases the abundance and species richness of soil bacteria, which can have several causes. Plant diversity and productivity affect soil microbial processes such as N mineralization [23] and the nitrification-denitrification pathway [24]. For example, increased crop diversity by intercropping of maize (Zea mays) with peanut (Arachis hypogaea) resulted in an increase in nifH, ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) amoA and narG abundance, whereas nirK, nirS and nosZ clade I remained unaffected [25]. Although soil microbial responses to agricultural management are highly complex [26] and not uniform among microbial groups [27], there is substantial evidence that agricultural intensification is one of the main drivers of observed losses in soil microbial diversity and connectivity [28][29][30]. The decreased complexity of intensive agricultural systems contributes to the loss of soil microbial biodiversity and is expected to negatively affect soil processes like nutrient cycling and retention [31,32]. The increase in system complexity associated with agroforestry is likely to alter soil microbial community structure and function, and thus soil-N-cycling.
Several studies have shown that temperate agroforestry systems affect N cycling and enzymatic activities in soils; however, associated changes in the abundance of microbial communities involved in soil-N cycling as well as soil fungi and bacteria remain to be elucidated. The main aim of this study was to test whether the abundance of genes encoding enzymes involved in soil-N cycling differ under temperate monoculture and agroforestry cropland and open grassland and agroforestry grassland. A further aim was to assess differences in the abundance of bacterial and fungal populations by quantifying bacterial 16S rRNA and fungal 18S rRNA genes. We hypothesized that the tree row in the agroforestry systems increases the above-and belowground litter input and nutrient cycling, which in turn i) changes the abundance of functional genes involved in nitrification and denitrification, and ii) increases relative fungal abundance. In contrast to prior studies analyzing substrate utilization patterns and enzymatic activities of soils under laboratory conditions, this is, to our knowledge, the first field-based study investigating differences in the abundance of genes involved in soil-N cycling between temperate monoculture and agroforestry cropland and open grassland and agroforestry grassland.

Study sites
We selected two sites at which monoculture cropland or open grassland was converted into agroforestry systems with an alley cropping design. The cropland site with a Phaeozem soil was located near Dornburg, Thuringia, Germany (51˚00' 40" N, 11˚38' 46" E, 289 m asl) (S1A Fig). The site has a mean annual precipitation of 608 ± 21 mm and mean annual air temperature of 9.9 ± 0.1˚C (mean ± SE during 1981-2010; climate station at Jena (station ID: 2444) of the German Meteorological Service). During our sampling year (2017), the site received 648 mm precipitation and mean temperature was 10.9˚C. In 2007, a monoculture cropland was converted into an agroforestry system by planting 12-m wide poplar rows (clone Max1; Populus nigra x P. maximowiczii), alternated with 48-m wide crop rows, forming an alley cropping pattern (S1C Fig). The tree rows we established from poplar cuttings, planted by hand using a dibble bar. In January 2015, the aboveground biomass of trees in the agroforestry system was harvested for the first time since establishment. An adjacent monoculture cropland was not converted and used as a reference with identical crops, management and fertilization rates. Both monoculture and agroforestry had the following crop rotation in the four years previous to our sampling: winter wheat (Triticum aestivum) (2014) / summer barley (Hordeum vulgare) (2015) / summer barley (2016) / oilseed rape (Brassica napus) (2017). On 24 August 2016, both the agroforestry crop rows and the reference monoculture cropland were fertilized with cattle manure (30 m 3 ha -1 , equivalent to 105 kg N ha -1 -19.5 kg P ha -1 -97.2 kg K ha -1 ). In accordance with common temperate agroforestry practice, the tree rows were not fertilized [33]; however, due to the close spatial proximity of the unfertilized tree rows to the fertilized crop rows, the trees may have taken up fertilizer.
The grassland site with Histosol and Anthrosol soils is located near Mariensee, Lower Saxony, Germany (52˚33' 49" N, 9˚28' 9" E, 42 m asl) (S1A Fig). This site has a mean annual precipitation of 661 ± 20 mm and mean annual air temperature of 9.6 ± 0.2˚C (1981-2010; climate station at Hannover (station ID: 2014) of the German Meteorological Service). In 2017, annual precipitation was 822 mm and annual mean temperature was 10.4˚C. At this site, part of a continuous open grassland (at least 30 years old) was converted into an agroforestry system in 2008, by planting 11-m wide rows of willow (clone Tora; Salix schwerinii x S. Viminalis), alternated with 48-m wide grass rows, also in an alley cropping pattern (S1B Fig). Willow cuttings were planted by hand using a dibble bar to protect the grass sward. In January 2016, the aboveground biomass of trees in the agroforestry system was harvested for the first time since planting. The grassland in both unconverted open grassland and the agroforestry system were managed in the same way: grasses were cut once a year and the last fertilizer application (15 m 3 ha -1 biogas plant residue containing 50 kg N ha -1 ) before sampling was on 10 November 2015. As was the case in the cropland agroforestry system, the tree rows were not fertilized [33], but the trees may have taken up applied fertilizer due to their close spatial proximity to the fertilized grass rows. The composition of the grass sward was the same in the two systems, consisting of Lolium perenne, Festuca pratensis, Phleum pratense, and Poa pratensis. monoculture system, soil samples were taken in the middle of each replicate plot. At the grassland site, we established in each of the two soil types three replicate plots in agroforestry and three in open grassland. At this site, soil sampling locations also included the tree row and the grass row at distances of 1, 4 and 7 m from the tree row, whereas in the open grassland soil samples were taken in the middle of each replicate plot (S1A Fig).
We took soil samples in the top 0.05-m depth on 3 May 2017 at the cropland site (Phaeozem soil) (252 days after fertilisation) and on 11 April 2017 at the grassland site (Histosol and Anthrosol soils) (518 days after fertilization). Three subsamples were taken per sampling location of each replicate plot. Soils were sampled using sterile 15-mL polypropylene Falcon tubes (Sarstedt Ag & Co, Nümbrecht, Germany) without any hand contact with the soil. The soil samples were frozen (-20˚C) in the field and immediately stored at -20˚C upon arrival at the laboratory. Starting the following day, the soil samples were freeze-dried for 72 h and vortexed in the same Falcon tubes with two tungsten carbide beads of 6 mm diameter for 60 s using a vortexer (HS120209, Heathrow Scientific, Vernon Hills, USA) at maximum power. Finely ground subsamples were pooled to obtain one mixed soil sample per sampling location of each replicate plot. Pooled soil samples were stored at -20˚C until DNA extraction.

DNA extraction from soil
DNA was extracted according to Beule et al.
[34] from 30 mg soil. Briefly, finely grounded soil was suspended in 1 ml of CTAB buffer with proteinase K and incubated at 42˚C and subsequently at 65˚C for 10min each. Two chloroform-isoamylalcohol extractions with an intermediate phenol extraction were conducted, and DNA was precipitated with polyethylene glycol and pelleted by centrifugation. DNA pellets were washed twice with ethanol, re-suspended in 50 μl of TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0), and incubated at 42˚C for 2 h to facilitate dissolution of DNA. Concentration and quality of the DNA were assessed by agarose gel electrophoresis using 1.7% (w/v) agarose gels, and DNA extracts were stored at -20˚C until analysis.

Real-time PCR
Different primers were used for real-time PCR (qPCR) to amplify parts of the bacterial 16S rRNA and fungal 18S rRNA gene as well as microbial genes encoding subunits of enzymes involved in nitrification: AOA amoA, AOB amoA and nxrB ( Standard curves for qPCRs were generated in two replicates using 1:3 serial dilutions in 0.5X TE buffer. Standards of Bacterial 16S rRNA, fungal 18S rRNA, nirK, nirS, nosZ clade I and nosZ clade II genes were obtained as described previously [46]. AOA amoA gene was obtained from an environmental clone, cloned in plasmid pGEM-T (Promega, Mannheim, Germany) and multiplied in Escherichia coli JM109. AOB amoA, nxrB, napA and narG gene standards were obtained from Nitrosomonas europeae DSM 28437, Nitrobacter winogradskyi DSM 10237 and E. coli DH5α (napA and narG), respectively. Genomic DNA of E. coli DH55α was extracted using a CTAB protocol [47]; Nitrosomonas europeae DSM 28437 was ordered as genomic DNA dissolved in TE buffer from the German Collection of Microorganisms and Cell Cultures (DSMZ). Nitrobacter winogradskyi DSM 10237 was grown in mixotrophic Nitrobacter medium (DSMZ Medium 756a) and genomic DNA was isolated from a 2-ml aliquot of the culture according to Wilson [48].
All qPCR reactions were carried out in a CFX 384 Thermocycler (Biorad, Rüdigheim, Germany) in 384-well microplates and conditions are listed in S2 Table. Amplification was performed with 1:100 dilutions of the DNA extracts in 4 μl reaction volume that contained the following: 3 μl mastermix (buffer (10 mM Tris-HCl, 50 mM KCl, 1.5 mM MgCl 2 , pH 8.3 at 25˚C); varying MgCl 2 concentrations (S2 Table); 100 μM of each deoxyribonucleoside triphosphate (Bioline, Luckenwalde, Germany); 0.3, 0.5 or 1.0 μM of each primer (S2 Table), 0.1X SYBR Green I solution (Invitrogen, Karlsruhe, Germany); 1 mg/ml bovine serum albumin; 0.03 u Hot Start Taq DNA Polymerase (New England Biolabs, Beverly, Massachusetts, USA)); and 1 μl template DNA solution or double-distilled H 2 O for negative controls. Amplification of each standard DNA concentration and negative control was performed in two replicates. For napA, narG, nirK, nirS and nosZ clade I genes, six touchdown cycles were run at the beginning, consisting of 20s denaturation at 94˚C, 30s annealing beginning at 66˚C, 63˚C, 63˚C, 58˚C and 65˚C for napA, narG, nirK, nirS and nosZ clade I genes, respectively, with a decrease of the annealing temperature by 1˚C per cycle, and 30s (15s for napA and narG) extension at 68˚C. To obtain melting curves, samples were heated to 95˚C for 60s and cooled to 55˚C for 60s followed by a temperature increase from 55˚C to 95˚C by 0.5˚C per cycle with continuous fluorescence measurement.

Soil properties
Physical and biochemical properties of soil were measured on soil samples taken in immediate proximity to the sampling locations for soil DNA extraction. Soil samples for the measurement of water-filled pore space (WFPS), plant-available P and total extractable N in the top 0.05-m depth were taken on same day that samples for DNA extraction were taken. WFPS was calculated from the measured gravimetric moisture content and soil bulk density, determined using the soil core method [49]. Plant-available P was the sum of resin-and bicarbonate-extractable P [50]. The concentration of P in the extracts was determined using an inductively coupled plasma-atomic emission spectrometer (ICP-AES, iCAP 6300 Duo VIEW ICP Spectrometer, Thermo Fischer Scientific GmbH, Dreieich, Germany). Total extractable N was extracted in the field by placing soil into bottles containing 150 mL 0.5 M K 2 SO 4 . Upon arrival in the laboratory, the bottles were shaken for one hour, and the extracts were filtered through pre-washed filter papers (7 μm nominal pore size) and kept frozen at -20˚C until analysis. Total extractable N was measured using continuous flow injection colorimetry (SEAL Analytical AA3, SEAL Analytical GmbH, Norderstedt, Germany). Since soil pH, soil organic C, total N and exchangeable K, Mg, Mn, and Na do not vary within a few years, we report values that were measured from samples taken in October-December 2016 for the Phaeozem soil (cropland) and in August 2016 for Histosol and Anthrosol soils (grassland) for the top 0.3-m depth. Soil pH was determined from a soil:water ratio of 1:4. Soil organic C and total N were determined using a CN analyzer (Elementar Vario El, Elementar Analysis Systems GmbH, Hanau, Germany); for soil samples with pH � 6.0 pre-treatment for the removal of carbonates was performed [51]. For all the exchangeable cations, soil was percolated with unbuffered 1 M NH 4 Cl and cations in the percolate were determined using ICP-AES. The values we report in S1

Statistical analysis
Each parameter was tested for equality of variance (Levene's test) and normality of distribution (Shapiro-Wilk's test). Differences among sampling locations within the agroforestry system (tree row, 1 m, 4 m and 7 m within the crop or grass row) and the monoculture or open grassland system for a soil type as well as among soil types for a management system were tested using one-way ANOVA with Tukey's HSD test for parameters with normal distribution and homogeneous variance; if otherwise, we used Kruskal-Wallis test with multiple comparison extension. Statistical significance was taken at p � 0.05, except for a few parameters with p > 0.05 � 0.08 that are mentioned as marginally significant. We conducted a network analysis based on Spearman's rank correlation test to assess the relationships between gene abundances and soil properties. Only correlations that were highly robust (p < 0.001) with a correlation coefficient (r) of r > |0.7| were reflected in the network analysis. This was performed using the "igraph" R-package [52]. Finally, a principle component analysis (PCA) of the gene abundances and soil properties was performed using the "factoextra" R-package [53]. All statistical analyses were conducted using R version 3.4.3.

Field-work permission
This study was a part of the project 'Sustainable intensification of agriculture through agroforestry' (SIGNAL) funded by the German Federal Ministry of Education and Research (BMBF). The fieldwork included soil sampling and was carried out on private land. The local land owners of the study sites gave permission to conduct the fieldwork on their sites. Besides this, no specific permissions were required to perform the fieldwork since the study sites were not located in a protected area. This field study did not involve endangered or protected species.

Abundance of bacterial 16S rRNA, fungal 18S rRNA and soil-N-cycling genes
Bacterial 16S rRNA genes were the most abundant across the two management systems of both cropland and grassland soils, ranging from log 10 9.3 to 10.3 copies g -1 soil (S3 Table) whereas fungal 18S rRNA genes ranged from log 10 7.7 to 8.8 copies g -1 soil. Genes involved in soil-N cycling ranged from log 10 4.6 (AOB amoA) to 9.8 (narG) copies g -1 soil. AOB and AOA amoA genes were within the range of the other soil-N-cycling genes, except in two replicate plots (tree row of the Histosol and Anthrosol soils (grassland)) where these genes were below the detection limit. The ratio of fungal 18S-to-bacterial 16S rRNA gene abundances ranged from 0.01 to 0.1 across the two management systems of the studied soils and was higher in the tree rows than in the crop or grass rows of the agroforestry and the monoculture and open grassland system (p � 0.01) (Fig 1).

Genes involved in nitrification
We quantified the gene abundance in two steps of nitrification (S2A Fig). Abundance of the AOA amoA gene in the Phaeozem soil (cropland) decreased with increasing distance from the tree row and was lowest in the monoculture system (p = 0.01) (Fig 2A). AOA amoA gene abundances were higher in the Phaeozem soil (cropland) compared to the grassland soils (Histosol and Anthrosol) in both management systems (p < 0.01). In contrast, AOB amoA gene abundance increased gradually with increasing distance from the tree row in the Phaeozem soil (cropland) (p = 0.06) (Fig 2B). In both grassland soils (Histosol and Anthrosol), no difference in AOA and AOB amoA gene abundances was observed between management systems. The ratio of AOA amoA to AOB amoA abundances was highly variable, ranging from 0.5 to 297. In 94% of the soil samples, AOA amoA genes outnumbered AOB amoA genes. Conversion to agroforestry alters the abundance of soil bacteria, fungi and soil-N-cycling genes The abundance of nitrite oxidoreductase gene (nxrB) in the Phaeozem (cropland) and Histosol (grassland) soils did not differ among the tree row and the different distances within the crop or grass row of the agroforestry and the monoculture or open grassland systems (S3 Fig). In the tree row as well as at all distances within the crop or grass row of the agroforestry, nxrB gene abundance was higher in the Histosol soil (grassland) than in the Phaeozem soil (cropland) (p < 0.01-0.03) (S3 Fig). The ratio of ammonia monooxygenase (AOA+AOB amoA) to nxrB gene abundances in the Phaeozem soil (cropland) was on average more than 30 times larger than those in the Histosol and Anthrosol soils (grassland) (Fig 2 and S3 Fig).

Genes involved in denitrification
We quantified the gene abundance that operate three specific steps in denitrification (S2B Fig). The abundance of the genes involved in nitrate reductase (napA and narG) did not differ among sampling locations within the agroforestry and the monoculture or open grassland system, except for the Phaeozem soil (cropland) where napA gene abundance in the tree row was higher than in the monoculture system (p = 0.05) (Fig 3A, Fig 3B). napA and narG gene abundances were higher in the Histosol soil (grassland) than the Phaeozem soil at all sampling locations (p < 0.01-0.06), except for napA in the tree row.
The abundance of nitrite reductase gene nirK in the agroforestry of Phaeozem soil (cropland) slightly decreased with increasing distance from the tree row and was lowest in the monoculture system (p = 0.07) (S4A Fig). The Histosol soil (grassland) showed higher nirK gene abundance than the Phaeozem soil at all sampling locations of the agroforestry system (p < 0.01-0.06). Gene abundances of nirS exhibited similar patterns as those of nirK gene among sampling locations and soil types (S4A Fig, S4B Fig). The ratio of nirK-to-nirS gene abundances did not differ among sampling locations within the agroforestry and the monoculture or open grassland system as well as among soil types (Fig 3C).
Lower abundance of nitrous oxide reductase gene nosZ clade I in the Phaeozem soil (cropland) than in the Histosol soil (grassland) was detected in all sampling locations of the agroforestry as well as in the monoculture or open grassland system (p < 0.01-0.04) (S4C Fig). In the tree row and at 4 m and 7 m within the grass row, nosZ clade I gene abundance was lower in the Anthrosol (grassland) than the Histosol soil (grassland) (p < 0.01) (S4C Fig). The abundance of nosZ clade II neither differed between management systems within each soil type nor among soil types, except between Phaeozem (cropland) and Anthrosol soils (grassland) for the tree rows (p = 0.07) (S4D Fig). The ratio of nosZ clade II-to-nosZ clade I gene abundances in the Phaeozem soil (cropland) was higher at 7 m within the crop row than in the tree row (p = 0.07) (Fig 3D). Additionally, the ratios of nosZ clade II-to-nosZ clade I gene abundances were also higher in the Phaeozem soil (cropland) than in the grassland soils (Histosol and Anthrosol) (p < 0.01-0.06) (Fig 3D).

Relationships between gene abundance and soil properties
Bacterial 16S rRNA gene abundance was positively correlated to soil organic C and total N and negatively correlated to soil pH across all replicate plots (Fig 4 and S4 Table). AOA amoA gene Conversion to agroforestry alters the abundance of soil bacteria, fungi and soil-N-cycling genes abundance showed positive correlations to WFPS, plant-available P and exchangeable K and Mg. The correlations of gene abundances of nxrB, napA, narG, nirK and nosZ clade I with soil properties showed similar patterns: positive correlations with soil organic C and total N and negative correlation with soil pH. Compared to nxrB and nirK, stronger correlations were observed for napA, narG and nosZ clade I with these soil properties. Fungal 18S rRNA, AOB amoA, nirS and nosZ clade II showed only weaker correlations with the measured soil properties (S4 Table) and hence are not depicted in this network analysis. The PCA revealed similar pattern between gene abundance and soil properties (S5 Fig).

Discussion
The increase in fungi-to-bacteria ratio in the tree row in all soil types (Fig 1) suggests that the tree component of agroforestry increased the relative fungal abundance. This finding concurred with the findings of Beuschel et al. [17] who reported larger fungal C-to-bacterial C ratios in the tree rows than the crop rows of poplar-based temperate agroforestry systems. As tree litter is more recalcitrant than crop or grass residues [54], increased tree litter likely accounts for the higher relative abundance of fungi in the agroforestry tree row. Moreover,

Fig 4. Network analysis of the relationships between gene abundances (white nodes) and soil properties (gray nodes).
Lines are based on the thresholds of Spearman's rank correlations, with r > |0.7| and p < 0.001 (S4 Table), across all replicate plots of paired temperate agroforestry and monoculture cropland or open grassland systems in all three soil types. Green lines represent positive and red lines represent negative correlations. The line's width is proportional to the absolute magnitude of the correlation coefficient. 16S = bacterial 16S rRNA gene abundance, 18S = fungal 18S rRNA gene abundance, AOA = ammonia-oxidizing archaea amoA gene abundance, AOB = ammonia-oxidizing bacteria amoA gene abundance, nosZI = nosZ clade I gene abundance, NosZII = nosZ clade II gene abundance, P = plant-available P, ex. N tot = total extractable N, N tot = total N, C org = organic C, WFPS = water-filled pore space. tillage practiced in cropland reduces the growth of fungal hyphae and disrupts mycorrhizal networks [55][56][57], which may have contributed to the reduction of fungal abundance in the arable land (Fig 1). Other factors, such as changes in microclimate and theabsence of fertilization within the tree row, may also have contributed to the observed pattern. The ratio of fungal 18S-to-bacterial 16S rRNA gene abundances does not equal to a biomass ratio, as the copy number of rRNA genes is taxa-specific [58]. Such ratio, however, allows to detect shifts in the composition of microbial communities across soils [59].
The larger AOA than AOB amoA gene abundances (Fig 2) suggests that archaea were the predominant prokaryotes involved in ammonia oxidation at our study sites. Significant contribution of archaea to nitrification has been observed in previous studies (e.g. [60][61][62][63]). In contrast, recent studies demonstrated that although AOA amoA genes may numerically dominate over AOB amoA, enzymes encoded by AOB amoA genes control nitrification [64][65][66][67]. In the cropland on Phaeozem soil, the opposing trends of AOA and AOB amoA gene abundance with increasing distance from the tree row in the agroforestry and the large AOB in the monoculture system suggest a niche differentiation of AOA and AOB, e.g. due to their preference for an ammonium source [68,69]. We attributed this pattern of AOA and AOB niche differentiation to the influence of trees and the application of N fertilizer both in the agroforestry crop row and in the monoculture system. AOA abundance was shown to increase by mineralized ammonium derived from soil organic matter or low ammonium concentrations in soils [70,71], whereas AOB is favoured by ammonium applied as mineral fertilizer [71,72]. Thus, long-term absence of mineral N fertilization and cultivation as well as tree litter input within the tree row of the agroforestry system may have contributed to the opposite trends of AOA and AOB amoA gene abundance. The influence of other soil factors on the abundance of AOA and AOB populations under field conditions is still not extensively investigated, and it is likely that a combination of soil factors also influence AOA and AOB abundance as well as niche partitioning.
Apart from the ammonium source, the positive correlations of AOA and AOB amoA gene abundance with WFPS and plant-available P, K and Mg (Fig 4) across sites, although with weaker correlations for AOB (S4 Table), indicated that soil moisture and macronutrients may also influence the abundance of AOA and AOB amoA genes. Szukics et al. [73] claimed that the population adaptability of AOA is greater than of AOB, which would enable AOA to adapt rapidly to changing environmental conditions. We found that AOA amoA gene abundance was more responsive to soil moisture than AOB, based on the correlation coefficients (S4 Table). Plant-available P as well as K and Mg are rarely measured when quantifying genes involved in soil-N cycling. Previous studies showed that AOA are positively influenced by NaHCO 3 and Bray-extractable available P, whereas the response of AOB to available P was opposite [74][75][76]. Our findings that both AOA and AOB amoA gene abundances increased with increasing plant-available P, exchangeable K and Mg indicated a link between ammonium cycling and these rock-derived nutrients, which was not driven by the general bacterial and fungal genes since their abundance were not correlated with these nutrients (Fig 4, S4  Table).
There are only a limited number of studies to which we can relate the relative abundance of nitrite oxidoreductase nxrB gene to amoA genes. A possible explanation for the higher ratio of amoA (AOA+AOB) to nxrB gene abundances in the cropland soil (Phaeozem) than those in the grassland soils (Histosol and Anthrosol) may be the selectivity of our nxrB primer pair (NxrB-1F/R) for Nitrobacter strains [39,77]. The functional group of nitrite-oxidizing bacteria (NOB) is composed of six genera, namely Nitrobacter, Nitrotoga, Nitrococcus, Nitrospina, Nitrospira and Nitrolancetus [78]. Attard et al. [79] demonstrated that changes in nitrite oxidation are contributed by the shifts between Nitrobacter-like and Nitrospira-like NOB rather than within Nitrobacter-like populations. Thus, greater ratios between amoA and nxrB gene abundances in the Phaeozem soil (cropland) may originate from non-Nitrobacter dominated NOB populations, which cannot be assessed by our primer pair.
The increased napA gene abundances in the tree row of the Phaeozem soil (cropland) ( Fig  3A) may indicate a more effective microbial removal of NO 3 in the tree row than the monoculture system. Root exudation of easily available C by trees is likely to facilitate microbial NO 3 removal through denitrification, dissimilatory NO 3 reduction and immobilization [80][81][82]. Apart from NO 3 uptake by tree roots, microbial NO 3 removal promoted by tree root exudates may additionally contribute to reduced NO 3 leaching in agroforestry systems. Overall, genes involved in denitrification were rather affected by soil type than management system ( Fig 3A, Fig 3B, S4 Fig). The stark differences in napA, narG, nirK and nosZ clade I gene abundances among soil types across management systems (Fig 3A, Fig 3B, S4A Fig, S4C Fig), i.e. Histosol (grassland) and Anthrosol (grassland) > Phaeozem (cropland), were mainly attributed to higher soil organic C and total N as well as lower soil pH of the grassland than the cropland (Fig 4, S1 Table). Similar correlations were found for nirS gene, although with lower correlation coefficients (S4 Table), which agree with previous findings [83,84]. Studies reporting the relationships between the abundance of denitrifying genes and soil organic C and total N are contradictory, which may be attributed to different soil types and soil management practices [83,[85][86][87][88]. Nevertheless, consistent with our findings, positive correlations between the abundances of napA, narG, nirK, nirS and nosZ clade I genes and soil organic C and total N have been observed in different ecosystems [85,86,88,89]. In addition, there are conflicting findings on the relationship between denitrification genes and soil pH, which relate to the range of pH covered in the studies. For example, increasing soil pH in acidic spruce forests exhibits a positive effect on nirK and a negative effect on nirS gene abundance [83], whereas the opposite was reported for grassland soils with a pH range from 6.4-7.1 [87]. Other studies even suggest that the abundance of both nir genes increases with increasing soil pH [84,90]. As the number of studies focusing on denitrifying genes and soil pH is limited and some studies cover rather narrow pH ranges, the effect of soil pH on the abundance of denitrification genes under field conditions is still not well understood. Recent phylogenetic studies identified a previously overlooked clade of microorganisms harbouring nosZ [45,91]. This clade (nosZ clade II) has attracted great attention as its abundance and phylogenetic diversity has shown to be critical to the reduction of N 2 O to N 2 in soils [92][93][94]. The nosZ clade II-type microorganisms cover a broad range of bacterial and archaeal phyla, whereas clade I-type microorganisms have been shown to consist exclusively of members of α-, β-and γ-proteobacteria [45]. The higher nosZ clade II-to-nosZ clade I ratio in the Phaeozem soil (cropland), signifying higher potential for N 2 O consumption, compared to the Histosol and Anthrosol soils (grassland) (Fig 3D) may suggest that this cropland soil may have a greater potential to consume atmospheric N 2 O to N 2 relative to its potential for complete to-N 2 reduction within the soil.

Conclusion
The trees in our agroforestry systems increased the fungi-to-bacteria ratio and altered the abundance of AOA and AOB amoA genes (particularly in the cropland on a Phaeozem soil), suggesting a niche differentiation. These may be due to the long-term absence of fertilization and cultivation and tree litter input within the tree row of agroforestry. Abundances of genes encoding for nitrate (napA and narG) and nitrite reductase (nirK and nirS) as well as nitrous oxide reductase gene nosZ clade I were less affected by the management system than by soil type. Overall, our results show that temperate agroforestry can alter the abundance of soil bacteria and fungi and soil-N-cycling genes compared to monoculture and open grassland. It should be noted that this study relies on a single measurement period, which does not allow temporal extrapolation of our findings. Future studies should thus focus on the temporal dynamics of the genes involved in soil-N cycling and its controlling factors in order to gain deeper understanding of the services provided by trees in agroforestry systems.