Ecology

The coefficient of variation of phytoplankton ((CV_P)) varies with the changes in environmental factors, namely, light, temperature and salinity and many more. The focus of our discussion will be on the variation of (CV_P) of phytoplankton.Case 1: (CV_P < 1) Measured (CV_P) values are 0.32, 0.37, 0.78 at the depth of 10 m, 50 m, 50 m of Region 3, Region 4 and Region 2 respectively. From Fig. 1c, we observe that for Region 3, concentrated mean of phytoplankton has escalated over a larger domain along the horizontal axis, while spread of phytoplankton is comparatively very low and constant for all times, whereas for Region 2 and Region 4 (Fig. 1,b,e), spread of phytoplankton is comparatively high, but, quantity of concentrated biomass is higher at Region 4 than Region 2, which is also supported by higher phytoplankton productivity at Region 4 than Region 2.Nature of spread of phytoplankton is obtained from the dynamics of normalized variance x of phytoplankton, which depends on (beta). At a fixed depth, x increases with increasing (beta) (Fig. 5b). For all regions where (CV_P1). Therefore, spread x remains comparatively low (Fig. 7b), whereas (p_0) is close to 1 (Fig. 7a), which causes (CV_P) to be less than 1 (Fig. 7c) at this zone.From above discussion we observe that when (varepsilon) belongs to (0.035, 0.1) and due to this range of (varepsilon), domain of (beta) reduces for a location, then (CV_P) remains less than 1 at that zone. These domains of (varepsilon , beta) are determined from nature of phytoplankton productivity at a location during the period of observation and nature of the spread of dominating class. It has been observed that in case of Region 3, during early summer season (May), the existing phytoplankton communities are Skeletonema Costatum, Navicula species and Pyraminonas Grossii36, for Region 4, the existing phytoplankton communities in Sep are diatom Skeletonema Costatum, Dinoflagellates, Raphidophytes and others35, whereas for Region 2, the existing classes in May are diatom Skeletonema Costatum, Raphidophytes and others35. But, for all three regions during corresponding time periods, most of the phytoplankton biomass is dominated by the diatom class, Skeletonema Costatum35,36. Spread of this phytoplankton class has a peculiar nature, which is influenced by its measure of stickiness (alpha), where (alpha in (0,,0.98))43. Now, during the period of observation, since the dominating class Skeletonema Costatum coexists with some other phytoplankton classes at all three regions, therefore range of its measure of stickiness (alpha) should belong to (0.02, 0.25) for these regions and depending on (alpha), scatteredness of Skeletonema Costatum has varied for these zones, that is, when (alpha) is high, scatteredness of Skeletonema Costatum reduces and when (alpha) is low, this scatteredness increases. In field observation, we have seen that, at Region 3, scatteredness of Skeletonema Costatum is very low in May 2011, whereas for Region 4 and Region 2, it is slightly higher in Sep 2007 and May 2011. For all three zones, (alpha) belongs to ((0.02,,0.25)) but its value has varied differently for each zone. If we consider (alpha) to be high for Region 3 in May 2011, then Skeletonema Costatum will be more sticky for that zone during that time period which will hinder the scatteredness. If we assume (alpha) to be slightly high for Region 2, Region 4 for corresponding time periods, then Skeletonema Costatum will be less sticky than Region 3 and scatteredness will be slightly higher for these zones by that time.In the model, spread due to scatteredness is controlled by low (beta) value. Therefore, ecologically it might be considered that during early summer at Region 3, (alpha) value was close to 0.25, which has caused Skeletonema Costatum to remain more sticky at that zone, as a result, spread was very low which represents low (beta) value. Similar ecological assumptions can be drawn in case of Region 2, Region 4, but the only difference is probably, for these two zones in summer and early spring season respectively, (alpha) was slightly low than Region 3. As a result, the dominating class Skeletonema Costatum was less sticky than Region 3 and spread due to scatteredness was slightly higher than Region 3 (Fig. S4b). Hence, differences in the nature of total biomass of a system, nature of productivity and finally nature of stickiness of dominating phytoplankton species cause high irregularity in phytoplankton distribution and produce low (CV_P) values for Region 2, Region 3 (Fig. 7c, Fig. S4c) and Region 4 (Fig. 8c, Fig. S4c). Case 2: (CV_P > 1)
In case of Region 4, at the depth of 50 m, (CV_P) remains 1.61 and 1.36 in Dec 2006 and Feb 2008 respectively. In Dec 2006, Feb 2008, due to very low productivity, range of (varepsilon) remains (0.35, 1.0) at Region 4, which generates larger domain of (beta) (considering total biomass and half saturation constant remain the same at Region 4 during both time periods Dec 2006 and Feb 2008). Since total biomass A is conserved, large value of (beta) indicates larger value of B, which ecologically implies spread of all fluctuating components of nutrient and phytoplankton remains higher. Therefore, in Dec 2006 and Feb 2008, spread of phytoplankton remains higher, whereas due to very low productivity, most of the total biomass A is dominated by nutrient biomass (n_0) and phytoplankton biomass (p_0) remains very low, that is, (p_0 More

Experimental design and crop managementThe study was conducted during 2019 in a field experiment on an arable soil (classified as Luvisols) in the deep root experimental facility at the University of Copenhagen, Denmark (Supplementary Table S4). The experiment was conducted with two diverse perennial deep-rooted species: the tap-rooted forage legume lucerne (Medicago sativa L. (cv. Creno); Family: Fabaceae) with the capacity to fix N2 and the intermediate wheatgrass (Thinopyrum intermedium; Family: Poaceae) kernza developed by the Land Institute (Salina, Kansas, USA). Kernza was initially sown on April 11th, 2015 and lucerne on September 9th, 2016 with a seeding density of 20 kg seeds ha−1. Every year, kernza was fertilized with NPK fertilizer (21:7:3; NH4:NO3 = 1.28) as a single dose in early spring (before the onset of plant growth). Kernza was harvested every year in August using a combine harvester and lucerne three times per year in June, August, and October. Plants were rainfed with a subsurface drain installed at both 1 and 2 m depth running between the plots.For each species, fixed frames of 0.75 m2 were inserted in the soil (ca. 5 cm) within each field plot. Specifically, three field plots of lucerne (with observable root nodulation) and kernza were used where each of the three kernza field plots contained two subplots of N fertilized kernza at 100 kg N ha−1 (K100) (i.e., the standard fertilization within this field) and N fertilized kernza at 200 kg N ha−1 (K200) (i.e., within the range of standard fertilization practices for kernza). Before the onset of plant growth, all plots received 15N (as 15NH4Cl; 98 atom%) in trace amounts (corresponding to 1 kg N ha−1) to trace N allocation from the surface to deeper layers.
13C/14C-CO2-labelingWithin each fixed frame, the 13C/14C-CO2-labeling was conducted using an atmospheric labeling chamber41. Labeling with C-tracers was done with multiple-pulse labeling (three times per week) over two months until first harvest (May 2nd to June 20th 2019). Glass beakers containing 13C labeled bicarbonate (0.1 g mL−1 labeling solution; 99 atom%), and 14C labeled bicarbonate (11 kBq mL−1 labeling solution) within a solution of NaOH (1 M) were added within each of the labeling chambers. Once chambers were sealed, hydrochloric acid (HCl; 2 M) was added to the labeling solution (in equivalent amounts) via a syringe promoting 14CO2/13CO2 evolution. Chambers remained sealed for one to three hours (between 9 am and 12 pm) depending on weather conditions (i.e., the duration and intensity of sunshine). The amount of added labeling solution sequentially increased with increasing plant growth (i.e., 5 mL per 20 cm increase in plant height) reaching a plant height of 100–120 cm at the termination of the labeling.Shoot, root, and soil samplingThe labeling plots (0.75 m2) were harvested on June 20th, 2019 to obtain the aboveground biomass of lucerne and kernza (K100 and K200). The aboveground biomass in addition to samples obtained from unlabeled parts of the field was directly stored at − 20 °C until drying at 105 °C for two days. For each plot and unlabeled samples, the plant biomass was homogenized and ball-milled for subsequent isotopic analyses.Soil cores to 1.5 m depth were taken inside all labeling plots, and cores were subdivided into four depth intervals: 0–25, 25–50, 50–100, and 100–150 cm. The soil coring was conducted in 25 cm intervals using a soil auger (6 cm inner diameter). Specifically, per depth three soil samples were taken and stored at 4–5 °C (ca. two days) and then immediately processed and stored at -20 °C until analyses. Roots, bulk soil and rhizosphere soil (adhering to the roots), were separated by sequential sieving of the soil with finer mesh sizes to 1 mm as described by Peixoto, et al.26. A subsample of the bulk soil (ca. 150 g) from each depth in all labeling plots was washed on a 250 µm sieve to recover root fragments for subsequent isotopic determination in unrecovered root fragments. Soil samples (and associated roots) from unlabeled parts of the larger field plots were used to determine natural abundance of 13C/14C/15N with depth. The collection of plant material complied with relevant institutional guidelines and seeds were gifted by University of Copenhagen.Determination of 13C/14C/15N enrichment, and C and N quantityFor each defined depth, samples of roots and soil were homogenized, freeze-dried (except PLFA samples that were stored at − 20 °C), and ground in a ball-mill for the determination of total C and N, 13C, 15N, and 14C activity. Total C, N, 13C, and 15N were measured with a FLASH 2000 CHNS/O Elemental Analyzer (Thermo Fisher Scientific, Cambridge, UK) combined to a Delta V Advantage isotope ratio mass spectrometer via a ConFlo III interface (Thermo Fisher Scientific, Bremen, Germany) at the Centre for Stable Isotope Research and Analysis (Georg August University Göttingen, Göttingen, Germany).All δ13C values are standardized to the Vienna PeeDee Belemnite international isotope standard and δ15N values standardized to the δ15N values of atmospheric N2. 13C and 15N enrichment is expressed as atom% excess as calculated by the atom% difference between the respective labeled and unlabeled samples. The 14C activity was determined by combustion in a Hidex 600 OX Oxidizer (Hidex, Turku, Finland) and counted on a liquid scintillation counter (Tri-Carb 3180TR/SL, PerkinElmer, Waltham, MA, USA). 14C enrichment is determined by the difference in the 14C activity (Bq g−1) between the respective labeled and unlabeled samples.Calculation of root C and net rhizodepositionThe amount of root C (mg C kg−1 soil) was calculated based on the root dry matter and C concentration divided by the quantity of soil sampled38. For the determination of net rhizodeposition, 14C was used due to lower detection limits in deeper soil layers42. A modified tracer mass balance approach described by Rasmussen, et al.43 with adjusted unrecovered root fragments41 was used to determine the net rhizodeposition based on the following equations where the %ClvR is the relative proportion of rhizodeposition expressed as the percent C lost via rhizodeposition:$${text{%ClvR}} = frac{{^{{{14}}} {text{C Soil (rhizosphere + adjusted bulk)}}}}{{^{{{14}}} {text{C bulk soil }} + ,^{{{14}}} {text{C rhizosphere soil}} + ,^{{{14}}} {text{C Root}}}} times 100.$$$${text{Net rhizodeposition}} = frac{{{text{%ClvR }} times {text{ root C content}}}}{{left( {100 – % {text{ClvR}}} right)}}$$The 14C soil content was the sum of the adjusted bulk soil 14C and rhizosphere 14C content for each soil sample. The 14C rhizosphere and bulk soil content for each soil sample were determined by multiplying the total quantity of C by the 14C enrichment of the soil. The adjusted bulk soil 14C content was calculated as the difference between the bulk 14C soil content by the 14C root washed content as determined by the multiplication of 14C enrichment in root fragments recovered from a subsample of soil by the total C content within the entire soil volume sampled. The 14C root content was determined by multiplying the total quantify of C in roots by the 14C enrichment. Similar equations were used to calculate the net rhizodeposition of N based on 15N enrichment within the soil and roots.Biomarker analysesPhospholipid fatty acid (PLFA)The analysis of PLFAs was done according to a modified protocol by Frostegård, et al.44 with a detailed description of the modifications provided by Gunina, et al.45. In brief, 25 μL of 1,2-Dinonadecanoyl-sn-Glycero-3-Phosphatidylcholine (C19:0) (1 mg mL–1) were added to each of the samples and used in the quantification of recovery of the phospholipids. The lipid fraction from 5–6 g of rhizosphere soil was extracted twice using a one-phase Bligh-Dyer extractant46 of chloroform, methanol (MeOH), and citrate buffer (pH 4) (1:2:0.8, v/v/v). To isolate the phospholipid fraction, a solid-phase extraction with activated silica gel and methanol elution was conducted. The derivatization into fatty acid methyl esters occurred via a sequential hydrolyzation with 0.5 mL sodium hydroxide (NaOH) (0.5 M) in MeOH for 10 min at 100 °C and methylation with 0.75 mL of boron trifluoride (BF3) (1.3 M) in MeOH for 15 min at 80 °C. An external standard stock solution containing 28 individual fatty acids (ca. 1 mg mL–1 per fatty acid) used in the quantification of PLFA content was simultaneously derivatized with the samples. The residues were dissolved in 185 μL of toluene, and 15 μL of the internal standard 2, tridecanoic acid methyl ester (C13:0) (1 mg mL–1) were added to each sample prior to measurement using an Agilent 7820A GC coupled to an Agilent 5977 quadrupole mass spectrometer (Agilent, Waldbronn, Germany). The sum of all PLFAs was used as a proxy of the living microbial biomass based on the direct relation between PLFAs and microbial biomass.Amino sugars (AS)Amino sugars were extracted according to a modified protocol by Zhang and Amelung47 with a detailed description of the procedure by Peixoto, et al.26. In brief, 0.8–1.5 g of freeze-dried rhizosphere soil were hydrolyzed with the addition of 11 mL of 6 M HCl for 8 h at 105 °C. Following hydrolysis, soil samples were filtered and HCl was removed via rotary evaporation at 45 °C to dry the filtrate. Prior to derivatization both iron precipitates and salts were removed from the filtrate and 25 μL of the internal standard 1, methylglucamine (MeGlcN) (1 mg mL–1) was added and used for quantification of recovery. The derivatization into aldononitrile acetates was conducted as described by Zhang and Amelung47. For the quantification of AS, an external standard stock solution containing the AS: N-acetylglucosamine (GlcN) (2 mg mL–1), N-acetylgalactosamine (GalN) (2 mg mL–1), N-acetylmuramic acid (MurN) (1 mg mL–1), mannosamine (ManN) (2 mg mL–1), and MeGlcN (1 mg mL–1) was derivatized and analyzed with the samples. The residues were dissolved in 185 μL of ethyl acetate-hexane (1:1, v/v), and 15 μL of the internal standard 2, tridecanoic acid methyl ester (1 mg mL–1), were added to the samples for measurement using an Agilent 7890A GC coupled to Agilent 7000A triple quadrupole mass spectrometer (Agilent, Waldbronn, Germany). Total amino sugars content was calculated as the summation of the four detected amino sugars: GlcN, MurN, GalN, and ManN.Amino acids (AA)Amino acids were extracted from both freeze-dried rhizosphere soil and root samples according to the protocol by Enggrob, et al.48. In brief, 0.8–3 g of rhizosphere soil and 0.02 g of root were hydrolyzed with the addition of 2 mL of 6 M HCl for 20 h at 110 °C to break the peptide bonds. Samples were subsequently purified via the removal of lipophilic and solid compounds by the addition of 4 mL n-hexane/dichloromethane (6:5, v/v) to the soil and root samples. Following centrifugation, the aqueous phase was filtered through glass wool and rinsed with 2 × 0.5 mL 0.1 M HCl into new glass tubes with the addition of 300 μL of the internal standard, norleucine (2.5 mM). The samples were freeze-dried and the residues dissolved in 1 mL 0.01 M HCl prior to the separation of amino acids and amino sugars (i.e., N containing compounds) on a polypropylene column with a cation exchange resin. The amino acids were eluted with a 2.5 M ammonium hydroxide solution and freeze-dried prior to derivatization of the amino acids as described by Enggrob, et al.48. For the quantification of AA, an external standard stock solution containing 14 AA was derivatized and analyzed with the samples. The amino acids were measured using a trace GC Ultra mounted with a TriPlus autosampler (Thermo Scientific, Hvidovre, Denmark) coupled via a combustion reactor (GC IsoLink, Thermo Scientific) to an isotope ratio mass spectrometer (Delta V Plus IRMS, Thermo Scientific). The total AA content of the rhizosphere soil and roots was based on the summation of the AA: alanine, Asx (asparagine and aspartate), Glx (glutamine and glutamate), glycine, isoleucine, lysine, phenylalanine, Pro/Thr (proline and threonine), serine, tyrosine, and valine.Compound-specific stable isotope probingTo determine the 13C enrichment of biomarkers, all raw δ13C were measured individually for AS and PLFA using a Delta V Advantage isotope ratio mass spectrometer via a ConFlo III interface (Thermo Fisher Scientific, Bremen, Germany). For AA, all raw δ13C were measured using a trace GC Ultra mounted with a TriPlus autosampler (Thermo Scientific, Hvidovre, Denmark) coupled via a combustion reactor (GC IsoLink, Thermo Scientific) to an isotope ratio mass spectrometer (Delta V Plus IRMS, Thermo Scientific). For each sample, chromatogram peaks identified based on retention times specific for the measured amino sugars, PLFA, and AA were integrated using Isodat v. 3.0 (Thermo Fisher Scientific). All raw δ13C values were corrected for dilution by additional C atoms added during the derivatization, amount dependence, offset, and drift (for PLFA samples)49,50,51. To determine the 13C incorporation into each biomarker, the 13C excess for each biomarker as determined by the difference between the 13C of the labeled and unlabeled biomarker was multiplied by the C content of the specific biomarker.Relative microbial stabilization (RMS)The relative microbial stabilization is based on the relation of rhizodeposited 13C in the PLFA and amino sugar pools as described in detail by Peixoto, et al.26. The underlying assumption is that 13C incorporation into the amino sugar pool indicates the transformation of rhizodeposited C into necromass52,53, and the 13C incorporation into the PLFA pool (i.e., the living microbial biomass) represents a temporary C pool as PLFAs are immediately exposed to degradation following cell lysis54. The relative microbial stabilization (RMS) is calculated as follows:$${text{Relative microbial stabilization}} = {text{log}}frac{{{text{Average weighted atom% }},^{{{13}}} {text{C excess AS}}}}{{{text{Average weighted atom% }},^{{{13}}} {text{C excess PLFA}}}}$$where the average weighted atom% 13C excess is determined by the total 13C incorporation divided by the total C content of the respective PLFA or amino sugar pools. Accordingly RMS  0 is indicative of higher stabilization of C based on the dominant entry of C into the microbial necromass. However, the RMS indicator does not imply the absolute stability of rhizodeposited C, but rather signifies the potential for microbial stabilization among contrasting experimental variables (i.e., depth and plant species).Molecular analysisDNA extractionFrom each sample, 0.5 g of freeze-dried rhizosphere soil was used for DNA extraction using the Fast DNA Spin kit for Soil (MP Biomedicals, Solon, OH, USA) according to the manufacturer’s protocol with a single modification. Following, the addition of Binding Matrix, the suspension was washed with 5.5 M Guanidine Thiocyanate (protocol from MP Biomedicals) to remove humic acids that could inhibit preceding polymerase chain reaction (PCR) steps. The DNA was eluted in DNase free water and purified using the NucleoSpin gDNA Clean-up kit following the manufacturer’s protocol (Macherey–Nagel, Düren, Germany). The purity and concentration of DNA were checked on Nanodrop and Qubit, respectively.Amplicon sequencingExtracted DNA was sent to Novogene Europe (Cambridge, United Kingdom) for library preparation and amplicon sequencing. For 16S rRNA gene amplicon sequencing of the V3-V4 regions, the primer pair 341 F and 806 R were used (Supplementary Table S5). To identify the fungal communities, we targeted the Internal Transcribed Spacer (ITS) Region 1, using the primer pair ITS1 and ITS2 (Supplementary Table S5). The constructed libraries were sequenced using a Novaseq 6000 platform producing 2 × 250 bp paired-end reads. Raw sequences were deposited in the NCBI Sequence Read Archive (Bioproject number PRJNA736561).Quantitative PCRCopy numbers of the 16S rRNA gene were determined by quantitative PCR (qPCR) using the primers 341F and 805R (Supplementary Table S5) on an AriaMX Real-Time PCR System (Agilent Technologies, Santa Clara, CA, USA). An external plasmid standard curve was made based on the pCR 2.1 TOPO vector (Thermo Fisher Scientific, Waltham, MA, USA) with a 16S rRNA gene insert amplified from bulk soil. The PCR reaction was performed in 20 µl reactions containing: 1 × Brilliant III Ultra-Fast SYBR green low ROX qPCR Master Mix (Agilent Technologies, Santa Clara, CA, USA), 0.05 µg/µl BSA (New England Biolabs Inc., Ipswich, MA, USA), 0.4 µM of each primer and 2 μl of template DNA. The thermal cycling conditions were 3 min at 95 °C followed by 40 cycles of 20 s at 95 °C and 30 s at 58 °C, and a final extension for 1 min at 95 °C. A melting curve was included according to the default settings of the AriaMx qPCR software (Agilent Technologies). The reaction efficiencies were between 97 and 102%. Fungal quantification was done by qPCR amplification of the Internal Transcribed Spacer 1 (ITS1) using the primers ITS1-F and ITS2 (Supplementary Table S5). A plasmid standard curve was made using the pCR 2.1 TOPO vector containing an ITS1 region from Penicillium aculeatum. Reaction mixture and cycling conditions were as described above for the 16S rRNA gene (Supplementary Table S5). The reaction efficiency was 84%.Quantification of functional genes involved in N cyclingThe five bacterial genes amoA, nirK, nirS, nosZ, and nifH coding for enzymes involved in N-cycling were quantified by qPCR on an AriaMx Real-Time PCR System (Agilent Technologies). Reaction mixtures and cycling conditions were as described above for the 16S rRNA gene (Supplementary Table S5). The standard curves were prepared as described in Garcia-Lemos, et al.55. The reaction efficiencies were in the range 87%-105%.Sequence processingRaw reads were treated using DADA2 version 1.14.156. In brief, reads were quality checked and primers were removed using Cutadapt v. 1.1557. We followed the protocol DADA2 using default parameters, with a few modifications. For 16S rRNA sequences, the forward and reverse reads were trimmed to 222 and 219 bp, respectively, while the maxEE was set to 2 and 5 for forward and reverse reads, respectively. Detection of amplicon sequence variants (ASVs) was done using the pseudo-pool option and forward and reverse reads were merged with a minimum overlap of 10 bp. Merged reads in the range of 395–439 bp were kept, as reads outside this range are considered too long or too short for the sequenced region. Taxonomy was assigned using the Ribosomal Database Project (RDP) classifier58 with the Silva database v.13859. For ITS region 1, quality filtered reads shorter than 50 bp were removed prior to merging the forward and the reverse reads, with maxEE set to two for both forward and reverse reads. During merging, the minimum overlap was set to 20 (default). Taxonomy was assigned with the RDP classifier using the Unite v. 8.2 database60 after removal of chimeras.As ITS region 1 has a variable length, reads can be lost during merging. Hence, to validate our dataset we ran only the forward reads through the DADA2 pipeline and compared the overall community structure with the dataset from the merging using a Mantel test. No significant changes were observed in the community structures between the two datasets (r = 0.99; p = 0.0001). To obtain the highest taxonomic resolution, the dataset based on the merged reads was used. Further analysis was done using the phyloseq v. 1.30.0 R package61.Statistical analysisAnalyses of variance (ANOVA) were conducted to examine the effects of N fertilized kernza at 100 kg N ha−1 (K100) and kernza at 200 kg N ha−1 (K200) as well as to test the effect of the deep-rooted plant species: kernza and lucerne, and soil depth on each of the dependent variables. An average across the two subplots within each of the three kernza field plots was used when measured variables did not significantly differ between K100 and K200. Subsequent pairwise comparisons of the means was conducted using the TukeyHSD post-hoc test. Homogeneity of variance and normality were confirmed (data log-transformed when required) for all comparisons using the Fligner-Killeen test of homogeneity of variances62 and the Shapiro–Wilk test of normality63. A permutational multivariate analysis of variance (PERMANOVA) using the Bray–Curtis dissimilarity matrix with the adonis function in the vegan R package was used to test the effect of K100 and K200, lucerne across both K100 and K200, and depth on the bacterial and fungal communities. The multivariate homogeneity of group dispersions or variances were confirmed for all comparisons using the function betadisper in vegan. The bacterial and fungal communities in response to the ascribed variables were visually represented as ordination plots with a Principle Coordinates Analysis (PCoA). Unique ASVs were defined for each depth and between K100, K200, and lucerne as ASVs only present in those samples belonging to a specific depth and treatment. Significance testing was conducted at p  More

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In this study we analysed how tree and arborescent palm species richness was related to aboveground carbon stock, commercially relevant timber stock, and commercially relevant NTFP abundance in tropical forests, and how these relationships were influenced by environmental stratification at different spatial scales. We found that species richness showed significant relationships with all three ecosystem services stock components, but its relationships were strongly influenced by variation across forest types and biogeographical strata. This is further explained below.Across the Guiana Shield, species richness showed a positive relationship with carbon stock and timber, but not with NTFP abundance. Although relationships only differed in significance among the biogeographical subregions, they differed in direction between terra firme forests and white sand forests. Species richness was positively related to carbon stock and timber stock in terra firme forests, whereas it was negatively related to NTFP abundance in white sand forests. The positive species-carbon relationship across forests of the Guiana Shield is in line with the effects described by hypotheses such as the ‘niche complementarity’ and ‘selection effect’10 and is in line with previous findings at regional spatial scales6,21. To our knowledge, the relationship between species richness and timber stock has not been previously analysed for tropical forests. Interestingly, the observed positive species-timber relationship in terra firme forests of the Guiana Shield contrasts with the negative species-timber relationship found for subtropical forests in both the U.S.A. and Spain20, although this may be explained by the difference in ecosystems. The non-significant species-NTFP abundance relationship across the Guiana Shield and the negative relationship within white sand forests seems to contradict previous findings. Steur et al.24 found a negative species-NTFP abundance relationship for tropical forests in Suriname. However, this negative relationship was found across multiple forest types, including flooded forests that had low species richness and high NTFP abundance. These flooded forests most likely influenced the species-NTFP abundance relationship across all forest types.In contrast to the relationship between species richness and carbon stock, no mechanism has been proposed for how species richness would influence commercial timber stock and NTFP abundance. Although our results suggest that species richness had a positive relationship with timber, the relationship was not found within multiple biogeographical subregions. For NTFP abundance, species richness did not contribute to explaining variation when variation across biogeographical subregions was accounted for (i.e. was included as an explanatory variable). We here tentatively propose that both commercial relevant timber stock and NTFP abundance are driven by variation in species floristic composition, rather than by species richness. For services such as commercial timber and NTFP provisioning, only a subset of all species is relevant (in this study, 9.4% of all morphospecies for timber and 3.8% for NTFPs), and such subsets are likely not random selections. For example, for Suriname, it was found that variation in commercially relevant NTFP abundance was driven by a particularly small selection of NTFP producing species with high abundances (referred to as ‘NTFP oligarchs’)24, and for commercial relevant timber stock, it is commonly known that selections tend to include more abundant than rare species. Additionally, as the relative abundance of species tends to vary across floristic regions in Amazonia, where, for example, certain species are dominant in particular forest types and biogeographical regions31,32, it can be expected that commercial timber stock and NTFP abundance are determined by floristic composition. In support, for NTFP abundance in Suriname tropical forests, Steur et al.24 found that floristic composition was a stronger predictor of NTFP abundance than species richness.Across all of Amazonia, species richness had a positive relationship with carbon stock, but only when variation among biogeographical regions was accounted for. The positive species-carbon relationship across Amazonia partly contrasts with previous findings at continental spatial scales11,13. When variation across climatic and/or edaphic variables was accounted for, Sullivan et al.13 found no significant species-carbon relationship across South-America, while Poorter et al.33 did find a positive relationship across Meso- and South-America. Here, we propose that accounting for differences among biogeographical regions can explain the previously found contrasts at continental spatial scales. In our dataset, for individual regions, we found either a positive relationship or a non-significant, but weakly positive, relationship between carbon stock and species richness (Fig. 2). However, when the data were aggregated across all regions, this resulted in a non-significant, and weakly negative, relationship. This reflects a known statistical phenomenon referred to as a ‘Simpson’s paradox’34, in which a relationship appears in multiple distinct groups but disappears or reverses when the groups are combined. Additional post-hoc tests of leaving one region out at a time showed that this pattern was not dependent of any particular biogeographical region. This is the first time that an analysis based on empirical data provides evidence for a Simpson’s paradox in species-ecosystem service relationships.It is likely that the observed differences in carbon stock across the biogeographical regions of Amazonia are influenced by multiple factors. For example, the biogeographical regions used in our analyses were recognised according to differences in substrate history, geological age and floristic composition, which could all contribute to variation in carbon stock. The substrate history and geological age of the biogeographical regions have been related to differences in soil fertility35, while multiple spatial gradients in floristic composition identified across the Amazon coincide with a spatial gradient in wood density28. However, further analysis is needed to obtain better insight into the relative contributions of these and other variables to explain the observed variation in carbon stock across the biogeographical regions. This requires data on multiple environmental variables, including floristic composition, climatic variables such as the length of the dry period, soil conditions, and intensity of disturbance.In our analyses, terra firme forests determined the relationship of species richness with the carbon stock, timber stock, and NTFP abundance across the datasets. Although this is most likely the effect of unequal sample sizes, with terra firme forests being the dominant forest type in terms of sample size (n = 130 vs. n = 21 for the Guiana Shield dataset; n = 257 vs. n = 26 for the Amazonia dataset), we expect that the observed relationships reflect the general pattern. Terra firme forests are the most dominant forest type in terms of geographical area32 and were representatively sampled. Regardless, the analyses per forest type had added value. The significant relationship between species richness and NTFP abundance in white sand forests across the Guiana Shield would otherwise have been overlooked.Due to the known scarcity of reliable and adequate information on which timber and NTFP species are being commercially traded36,37,38,39, we used a fixed set of timber and NTFP species to apply across the Guiana Shield plots. However, in reality, timber and NTFP species can be expected to vary according to socio-economic factors, such as culture, access, and harvest costs, which may change over space and time. Therefore, estimates of timber stock and NTFP abundance can be expected to differ across spatial gradients, and thus, their possible relationships with species richness cannot be easily generalised. To circumvent this, timber stock and NTFP abundance would have to be estimated on the basis of ‘flexible’ species selections that can change according to local socio-economic contexts. To this end, detailed information on both commercially relevant timber and NTFP species is urgently needed. Yet, for our study area, we did not observe major differences in selected species, and we included broad selections of species, which should make timber stock and NTFP abundance robust against small deviations in species selection. It must be noted that our approach of quantifying commercial relevant timber stock and NTFP abundance does not consider the value of timber and NTFPs for subsistence use. In addition, NTFPs can also be derived from other growth forms, such as lianas, shrubs and herbs. Last, because NTFP production data was not available we used NTFP abundance as a proxy for NTFP stock, following similar assessments of NTFP stock 24,40. A limitation of this approach is that each NTFP species individual has an equal contribution to NTFP stock, whereas it can be expected that large individuals may have a larger contribution than smaller individuals and that production volumes can differ for different types of NTFPs, for example barks vs. seeds.Our findings illustrate the importance of considering environmental stratification and spatial scale when analysing relationships between biodiversity and ecosystem services. First, environmental stratification can help detect relationships that are otherwise obscured by environmental heterogeneity. For example, although the association between species richness and carbon stock across Amazonia was relatively weak (explaining ~ 3% of total variation vs. ~ 15% in the Guiana Shield) and was obscured by variation in carbon stock across biogeographical strata, by using environmental stratification the positive relationship remained detectable. Second, environmental heterogeneity tends to vary with spatial scale; therefore, its importance needs to be checked according to spatial scale. For example, at the regional scale of the Guiana Shield, biogeographical subregions explained a moderate amount of variation in carbon stock (~ 20%), while at the spatial scale of Amazonia, biogeographical regions explained more than half of total variation in carbon stock (~ 55%). Such an increase and ultimate importance of variation across biogeographical strata might also explain the absence of a significant relationship between species richness and carbon stock across African and/or Asian tropical forests as reported by Sullivan et al.13.In our analyses, we found evidence of a positive relationship between species richness and carbon stock across and within Amazonia. This supports the notion that win–win scenarios are possible in conservation approaches, where, for example, REDD+ can be expected to help conserve tropical forests that contain large amounts of carbon stock and high concentrations of species9. However, we conclude that species richness is not always a strong predictor of biomass-based ecosystem services. In our analyses, NTFP abundance was not driven by species richness, and we ultimately expect the same for timber stock. We expect that differences in floristic composition, linked to differences across forest types and biogeographical strata, will be more relevant than species richness in explaining variation in timber stock and NTFP abundance. This would mean that conserving timber and NTFP related ecosystem services requires the development of additional region-specific strategies that account for differences in floristic composition. For example, areas with high concentrations of timber or NTFPs could be considered in the designation of multiple use protected areas41, such as the extractive reserves in Brazil, or be included as ‘high conservation value areas’ (HCVAs) in sustainable forest management certification42. More

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FennelThe main effect of fertilization (F) significantly impacted all measured parameters of fennel. Intercropping (I) pattern affected all parameters except plant height and 1000-seed weight. Significant I × F interactions occurred for umbel number, seed yield, essential oil content (EO), EO yield, oil content, and oil yield (Table 2).Table 2 Analysis of variance for the effect of cropping pattern and fertilization on evaluated traits in fennel.Full size tablePlant heightThe tallest fennel plants (125.8 cm) occurred in the sole cropping (Fs), while the shortest plants (98.6 cm) occurred in 1F:2FG. Across intercropping patterns, the Fs treatment had 28%, 14%, 11% taller fennel plants than 1F:2FG, 2F:2FG, and 2F:4FG, respectively (Fig. 1A). Compared to the unfertilized control, HA and BFS increased fennel plant height by 10% and 13%, respectively (Fig. 1B).Figure 1Means comparison for the main effects of cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on plant height (A), and fertilization [C (control), HA (humic acid), BFS (biofertilizers)] on plant height (B) and 1000-seed weight (C) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image1000-seed weightCompared to the unfertilized control (3.9 g per 1000 seeds), BFS and HA increased the 1000-seed weight of fennel by 24.1% and 14.5% (4.9 and 4.5 g per 1000 seeds), respectively (Fig. 1C).Umbel numberThe fennel sole cropping fertilized with HA produced the most umbels of fennel (51.5), while 2F:4FG without fertilization produced the least (32). Averaged across fertilizer types within each intercropping system, 1F:2FG, 2F:2FG, and 2F:4FG had 21.1%, 16.3%, and 26.7% fewer umbels than fennel sole cropping, respectively. Across intercropping systems, HA and BFS increased the umbel number by 17.8% and 16.5% compared with the unfertilized control, respectively (Fig. 2A).Figure 2Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on umbel number (A) and seed yield (B) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageSeed yieldThe different intercropping patterns had lower fennel seed yields than fennel sole cropping. Sole cropping fertilized with BFS and HA produced the highest fennel seed yields (2233 and 2209 kg ha–1, respectively), followed by unfertilized sole cropping (1960 kg ha–1). The lowest seed yields occurred in the unfertilized controls in 1F:2FG (933 kg ha–1) and 2F:4FG (1033 kg ha–1). Averaged across fertilization treatments, fennel seed yield in 1F:2FG, 2F:2FG, and 2F:4FG decreased by 41.7, 26.8, and 36.3%, respectively, compared to fennel sole cropping (Fs). Averaged across intercropping patterns, HA and BFS increased fennel seed yield by 33.3% and 39.5% compared with the unfertilized control, respectively (Fig. 2B).Essential oil content and yieldThe different intercropping patterns produced higher EO contents of fennel than fennel sole cropping. The highest absolute EO content of fennel (4.22%) occurred in 2F:2FG fertilized with BFS, although this did not statistically differ from the 2F:2FG fertilized with HA (4.04%) or 2F:4FG fertilized with HA or BFS (3.8% and 4.00%, respectively) (Fig. 3A). The lowest EO contents occurred in the unfertilized control (2.38%), HA (2.55%), and BFS (2.57%) in the Fs system. Averaged across fertilization treatments, the EO content of fennel in 1F:2FG, 2F:2FG, and 2F:4FG increased by 36%, 52%, and 44% compared to fennel sole cropping, respectively. Within each intercropping pattern, and with the exception of Fs, the HA and BFS treatments had higher EO contents of fennel, none of which significantly differed, increasing by 25% and 29%, respectively (Fig. 3A).Figure 3Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [Fs (fennel sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on essential oil content (A), essential oil yield (B), oil content (C), and oil yield (D) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageMaximum EO yields of fennel occurred with HA or BFS applied in 2F:2FG (65.2 and 66.6 kg ha–1) and 2F:4FG (60.7 and 65.5 kg ha–1), respectively, while the lowest EO yields occurred in the unfertilized control in 1F:2FG (27.2 kg ha–1), 2F:2FG (33.2 kg ha–1), and 2F:4FG (32.1 kg ha–1). Averaged across intercropping patterns, the EO yield of fennel increased by 66.1% and 74.7% with HA and BFS, respectively (Fig. 3B).Fennel essential oil compositionGC–FID and GC–MS analyses identified 14 components in the fennel EO (representing 97.4–99.9% of the total composition) (Table 3), with the main constituents being trans-anethole (78.3–84.85%), estragole (3.02–7.17%), fenchone (4.14–7.52%), and limonene (3.15–4.88%). The highest percentage of (E)-anethole, estragole, and fenchone occurred in 2F:2FG with BFS. The highest limonene content occurred in 2F:4 FG with HA. The relative contents of trans-anethole, fenchone, and limonene increased by 3.9%, 16.6%, and 8.4% compared with fennel sole cropping. Notably, the contents of most compounds increased with HA and BFS. Compared to the unfertilized control, trans-anethole, fenchone, and limonene contents increased by 2.9%, 21.5%, and 7.9% with BFS and 2.3%, 22.4%, and 11.9% with HA, respectively (Table 3).Table 3 Proportion of fennel essential oil constituents under different cropping patterns and fertilization.Full size tableFennel oil content and yieldAmong the studied treatments, the highest fennel oil content occurred with HA or BFS application in 1F:2FG (16.3% and 16.6%) and 2F:2FG (16.3% and 17.4%), respectively. The lowest fennel oil contents occurred in the unfertilized control, HA, and BFS treatments (12.5%, 12.8%, and 12.9%, respectively) under fennel sole cropping, and the unfertilized control in 2F:4FG (12.6%). Averaged across fertilizer treatments, fennel oil content in 1F:2FG, 2F:2FG, and 2F:4FG increased by 22.8%, 26.0%, and 12.6% compared with fennel sole cropping, respectively. Across intercropping patterns, HA and BFS increased fennel oil content by 13.5% and 16.5%, respectively (Fig. 3C).The maximum oil yield of fennel (318.6 kg ha–1) occurred in 2F:2FG fertilized with BFS, while the lowest oil yield (129.3 kg ha–1) occurred in 1F:2FG without fertilization. Across intercropping patterns, HA and BFS increased fennel oil yield by 50.8% and 62.6%, respectively (Fig. 3D).Oil compoundsGC–FID and GC–MS analyses identified nine constituents that represented 94.3–97.9% of the total fennel oil composition. The main oil constituents were oleic acid (39.2–48.3%), linoleic acid (17.1–24.8%), stearic acid (10.9–15.4%), lauric acid (10.1–14.00%), and arachidic acid (2.2–3.4%). The highest oleic and linoleic acid contents occurred in 2F:4FG and 2F:2FG fertilized with BFS, respectively. Across fertilizer treatments, oleic and linoleic acid contents increased by 6% and 21%, respectively, under different intercropping patterns compared with fennel sole cropping. Across systems, HA and BFS enhanced oleic acid content by 1.8% and 8% and linoleic acid by 7.9% and 8.2%, respectively, compared with the unfertilized control. The highest percentage of stearic and lauric acids occurred in the unfertilized control of fennel sole cropping. Conversely, the lowest stearic and lauric acid contents occurred in 2F:2FG and 2F:4FG fertilized with BFS, 16.1% and 14.2% higher than fennel sole cropping, respectively. Finally, HA and BFS decreased stearic acid content by an average of 5.4% and 7.2%, respectively (Table 4).Table 4 Proportion of fennel oil constituents under different cropping patterns and fertilization.Full size tablePhenolic compoundsThe main phenolic compounds of fennel were chlorogenic acid (10.4–15.3 ppm), quercetin (7.0–17.2 ppm), and cinnamic acid (4.1–8.9 ppm). The highest chlorogenic acid and quercetin contents occurred in 2F:2FG fertilized with BFS and HA, respectively, while the lowest contents occurred in the fennel sole cropping system without fertilizer. Averaged across the three intercropping patterns, the chlorogenic acid and quercetin contents were 18.5% and 80.1% higher than the fennel sole cropping system. The chlorogenic acid and quercetin contents increased by 13% and 17% with BFS and 22% and 15% with HA, respectively (Table 5).Table 5 Concentration of phenolic compounds in fennel under different cropping patterns and fertilization.Full size tableFenugreekThe main effects of intercropping (I) pattern (C) and fertilizer (F) were significant for all parameters analyzed in fenugreek. Significant I × F interactions occurred for plant height, pod number per plant, seed yield, oil content, and oil yield of fenugreek (Table 6).Table 6 Analysis of variance for the effects of cropping patterns and fertilization on evaluated traits in fenugreek.Full size tablePlant heightThe 2F:2FG intercropping system fertilized with BFS produced the tallest fenugreek plants (63 cm), followed by 1F:2FG with BFS (53.3 cm) and 2F:4FG with BFS (56 cm), and 2F:2FG with HA (55 cm). The unfertilized control produced the shortest fenugreek plants (42 cm) in the sole cropping. Most fertilizer treatments across different intercropping patterns produced taller fenugreek plants than their sole cropping counterparts. Across fertilizer treatments, 1F:2FG, 2F:2FG, and 2F:4FG produced 16.2%, 26.8%, and 14.6% taller fenugreek plants than sole cropping, respectively. Across cropping patterns, BFS and HA increased fenugreek plant height by 5.7% and 15.2% compared with the unfertilized control, respectively (Fig. 4A).Figure 4Means comparison for the interaction effect of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on plant height (A) and pod number per plant (B) of fenugreek. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imagePod number per plantThe fenugreek sole cropping with BFS and HA and 2F:4FG with BFS produced the most pods per plant (21.3, 20.3, and 20, respectively), while the unfertilized controls in 1F:2FG, 2F:2FG, and 2F:4FG produced the least (11.6, 12, and 13.3, respectively). Across fertilization treatments, 1F:2FG, 2F:2FG, and 2F:4FG had 30.1%, 25.6%, and 14.3% fewer pods per plant, respectively, than the fenugreek sole cropping system. Across cropping systems, HA and BFS increased pod number per plant in fenugreek by 25% and 33%, respectively, relative to the corresponding sole cropping (Fig. 4B).Seed number per podAcross fertilization treatments, fenugreek sole cropping produced the most seeds per pod (7.09), followed by 2F:4FG (6.02), 2F:2FG (4.93), and 1F:2FG (4.41) (Fig. 5A). In relative terms, sole cropping produced 60.5%, 43.9%, and 17.6% more seeds per pod than 1F:2FG, 2F:2FG, and 2F:4FG (Fig. 5A). Across cropping patterns, BFS and HA increased seed number per pod by 8.1% and 17.4% compared with the unfertilized control, respectively (Fig. 5B).Figure 5Means comparison for the main effects of cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on seed number per pod (A) and 1000-seed weight (C), and fertilization [C (control), HA (humic acid), BFS (biofertilizers)] on seed number per pod (B) and 1000-seed weight (D) of fennel. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image1000-seed weightAmong different cropping patterns, sole cropping and 1F:2FG produced the highest (10.45 g) and lowest (8.34 g) fenugreek seed weights, respectively. In relative terms, fenugreek sole cropping produced 25.3%, 21.8%, and 12.4% higher seed weights than 1F:2FG, 2F:2FG, and 2F:4FG, respectively (Fig. 5C). Across cropping patterns, BFS and HA increased fenugreek seed weight by 3.7% and 5.7% compared with the control, respectively (Fig. 5D).Seed yieldMeans comparisons showed that sole cropping produced higher fenugreek seed yields than intercropping patterns. Sole cropping with BFS (1240 kg ha–1) and HA (1217 kg ha–1) produced the highest seed yields followed by the unfertilized control (Fig. 6A). The unfertilized control in 1F:2FG (437 kg ha–1) and 2F:2FG (467 kg ha–1) produced the lowest fenugreek seed yields. In all cases, and within each cropping pattern, BFS and HS produced higher fenugreek seed yields than the unfertilized control. As a result, BFS and HA increased fenugreek seed yield by 25.2% and 31.5% compared with the unfertilized control, respectively (Fig. 6A).Figure 6Means comparison for the interaction effects of fertilization [C (control), HA (humic acid), BFS (biofertilizers)] and different cropping patterns [FGs (fenugreek sole cropping), 1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] on seed yield (A), oil content (B), and oil yield (C) of fenugreek. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size imageOil content and yieldThe 2F:2FG cropping pattern with BFS produced the highest fenugreek oil content (8.3%), while the unfertilized control in sole cropping produced the lowest (5.9%). Across fertilizer treatments, 1F:2FG, 2F:2 FG, and 2F:4 FG produced 11.7%, 18.5%, and 15.7% higher fenugreek oil contents than sole cropping, respectively. In the 2F:2FG and 2F:4FG cropping patterns, BFS produced higher oil content (%) than HA. As a result, across cropping patterns, HA and BFS increased fenugreek oil content by 12.3% and 19.4%, respectively (Fig. 6B).Sole cropping with HA and BFS and 2F:2FG with BFS produced the highest fenugreek oil yields (77.1, 80.0, and 74.4 kg ha–1, respectively), while the unfertilized controls in 1F:2FG and 2F:4FG produced the lowest (27.51 and 29.8 kg ha–1, respectively). The 1F:2FG, 2F:2FG, and 2F:4FG cropping patterns produced 45.9%, 20.7%, and 41.5% lower fenugreek oil yields than fenugreek sole cropping, respectively. Moreover, except for sole cropping, BFS produced the highest fenugreek oil yield, followed by HA and the unfertilized control (Fig. 6C).Oil compoundsGC–FID and GC–MS analyses identified seven constituents (representing 91.09–99.27% of the total composition) in fenugreek oil. The main oil constituents were linoleic acid (26.1–37.1%), linolenic acid (16.9–22.4%), oleic acid (15.1–21.2%), palmitic acid (11.2–17.1%), lauric acid (5.0–12.3%), and myristic acid (3.1–6.4%). The highest linoleic and oleic acid percentages occurred in 1F:2FG and 2F:4FG with BFS. The 1F:2FG cropping pattern with BFS also had the highest linolenic acid percentage. The fenugreek sole cropping system without fertilization (control) had the lowest content of these three compounds. The intercropping patterns had 17%, 18.2%, and 17.1% higher oleic, linoleic, and linolenic acid contents than fenugreek sole cropping. In addition, HA and BFS increased oleic acid content by 15.6% and 8.8%, linoleic acid content by 12.8% and 7%, and linolenic acid content by 7.5% and 12.9%, respectively. Fenugreek sole cropping without fertilization produced the highest lauric acid and palmitic contents, 29.33% and 22.81% higher than the intercropping patterns (Table 7).Table 7 Proportion of fenugreek oil constituents under different cropping patterns and fertilization.Full size tablePhenolic compoundsThe main phenolic compounds in fenugreek were chlorogenic acid (2.01–5.49 ppm), caffeic acid (2.42–4.93 ppm), quercetin (1.98–4.45 ppm), comaric (1.09–2.43 ppm), apigenin (1.97–2.99 ppm), and gallic acid (1.76–2.92 ppm). The 2F:2FG cropping pattern with HA produced the highest quercetin and gallic acid contents, and 2F:4FG with HA produced the highest chlorogenic and caffeic acid contents. The 2F:2FG and 2F:4FG cropping patterns with BFS produced the highest comaric and apigenin contents, respectively. In contrast, fenugreek sole cropping without fertilization produced the lowest contents of the abovementioned compounds (Table 8).Table 8 Proportion of fenugreek concentration of phenolic compounds under different cropping patterns and fertilization.Full size tableLand equivalent ratio (LER)The 2F:4FG and 2F:2FG intercropping patterns treated with BFS had the highest partial LERs for fennel (0.82) and fenugreek (0.72), respectively. In addition, 2F:2FG with BFS and 1F:2FG without fertilization produced the highest (1.42) and lowest (0.86) total LERs, respectively (Fig. 7).Figure 7Partial and total land equivalent ratio (LER) for seed yields of different fennel and fenugreek intercropping patterns [1F:2FG, 2F:2FG, 2F:4FG (ratios of fennel and fenugreek in the intercropping patterns)] and fertilization [C (Control), HA (humic acid), BFS (biofertilizers)]. Different lower-case letters above the bars indicate significant (p ≤ 0.05) differences.Full size image More

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