Above- and belowground biodiversity jointly tighten the P cycle in agricultural grasslands

The study investigates how biodiversity, both aboveground (plant species richness) and belowground (arbuscular mycorrhizal fungi, AMF), influences phosphorus (P) cycling in grasslands. Prior research shows biodiversity enhances productivity and tightens nitrogen cycles, but effects on the P cycle are less clear due to P’s low solubility and reliance on plant–microbe interactions, particularly mycorrhizal symbioses. The authors hypothesize that higher plant and mycorrhizal diversity lead to more complete P resource exploitation through plant–microbe-mediated mechanisms. They test whether mechanisms found in biodiversity experiments also operate in long-established agricultural grasslands where management intensity (fertilization, mowing, grazing) filters biodiversity and may modify P cycling.

• High biodiversity generally increases ecosystem functioning and resource exploitation via facilitation and niche complementarity. Nitrogen losses (e.g., leaching) decrease with higher plant diversity, tightening the N cycle, but comparable P reductions are not typically observed due to P’s low solubility and different plant acquisition strategies.
• P acquisition in temperate grasslands largely depends on AMF symbiosis; plant and AMF species identity and richness can influence productivity and P transfer.
• P can also be supplied indirectly via microbial turnover and enzyme-mediated mineralization of soil organic P. Individual processes (AMF-mediated uptake, microbial P turnover, enzymatic P release) have been shown to increase with plant species richness.
• In managed agroecosystems, biodiversity and management interact strongly. High management intensity generally reduces plant and AMF richness via environmental filtering, while favoring highly productive, exploitative species. Biodiversity–productivity effects can persist under fertilization in short-term experiments, yet species composition shifts under different management regimes complicate long-term, fully crossed designs.

Design overview:

• Two complementary datasets were used: (1) a controlled grassland biodiversity experiment (The Jena Experiment) with 76 plant mixtures spanning richness levels (1, 2, 4, 8, 16 species; 60-species mixtures excluded from analysis), extensively managed (mown twice yearly, no fertilization), and (2) 100 permanent agricultural grasslands from two regions (Schwäbische Alb and Hainich-Dün, Germany) along a gradient of management intensity (fertilization, mowing frequency, livestock units), classified as meadows, pastures, or mown pastures.

Key variables and measurements:

• Plant species richness: assessed on 9 m² in the experiment (May 2014). In agricultural grasslands, assessed on 16 m² (2011, 2014), then adjusted to 9 m² using a scaling factor (0.91) derived from nested subplot surveys.
• Aboveground plant biomass and P stocks: peak biomass harvested (experiment: May 2014; agricultural grasslands: May–June 2011 and 2014). Plant P concentrations measured by digestion/ICP-OES, NIRS (calibrated), or XRF (validated equivalence). Plant P stocks calculated as May biomass times P concentration times annual mowing frequency (acknowledged as likely overestimation to ensure comparability).
• Soil sampling: Experiment—Corg (April 2014, 0–0.15 m), P fractions (September 2013). Agricultural grasslands—joint campaigns in May 2011 and 2014 (0–0.10 m). Bulk density measured and used to convert concentrations to stocks.
• Soil P fractions: Hedley–Kuo sequential extraction into bioavailable (NaHCO3), moderately labile (NaOH), and mineral (HCl) P; inorganic P (Pi) by molybdate blue, total dissolved P by ICP-OES; organic P by difference (NaHCO3, NaOH).
• Microbial P stocks: hexanol fumigation with anion-exchange membrane extraction, spike recovery, corrected by extraction efficiency factor (divide by 0.46). Stocks computed using bulk density (0–0.15 m in experiment).
• P exploitation: proportion (%) of organismic P (aboveground plant + microbial P stocks) relative to the sum of organismic plus labile and moderately labile soil P stocks.
• Soil enzymes and microbial community: Phosphomonoesterase (experiment) measured via colorimetric or fluorimetric assays (agricultural sites). Fungal:bacterial PLFAs (experiment) assessed via lipid extraction and biomarker PLFAs.
• AMF community: Experiment—18S rRNA amplicon sequencing (454) with primers FR1/FF390 (2010 samples). Agricultural grasslands—nested PCR (GlomerWTO/Glomer1536; NS31/AML2) and Illumina MiSeq; taxonomic assignment via MaarjAM database; AMF species richness (incl. OTUs) and relative abundance calculated (rare taxa <5 plots excluded).
• Management intensity: annual farmer questionnaires (livestock units ha−1 yr−1, mowing events yr−1, N fertilizer kg ha−1 yr−1); mineral P generally not applied (two plots received 25 kg ha−1 in 2011); means across 2011 and 2014 used.

Statistical analysis:

• Data transformations applied as needed; Pearson correlations with BH correction.
• Piecewise structural equation modeling (SEM) to test hypothesized causal pathways linking plant richness, AMF richness/abundance, soil Corg stocks, microbial P stocks, plant P stocks, management intensity, and P exploitation. Random effects: block (experiment), region (agricultural grasslands). Spatial autocorrelation checked via residuals vs. coordinates. Model fit evaluated with Fisher’s C; missing paths added based on tests of directed separation to optimize fit.

Model constructs:

• Biodiversity experiment SEM: hypothesized positive pathways from plant richness to AMF richness/abundance, to soil Corg, to microbial and plant P stocks, and ultimately to P exploitation.
• Agricultural grasslands SEM: extended with direct management effects on plant richness and P exploitation; optimized by adding direct management paths to Corg and plant P stocks and considering AMF-mediated pathways.

• Biodiversity experiment:

  • P exploitation increased with plant species richness, mediated by higher soil Corg stocks and microbial P stocks, indicating coupling of C and P cycles via enhanced microbial activity and nutrient homeostasis. Plant P stocks also increased with plant richness due to higher biomass, not higher tissue P concentrations.
  • AMF species richness and relative abundance correlated with plant richness but showed no direct significant paths to plant P stocks or P concentrations in this system.
  • SEM fit was strong: Fisher’s C = 7.12, df = 14, p = 0.93.

• Agricultural grasslands:

  • Initial SEM with only management affecting plant richness did not fit; optimized model indicated that management directly influenced multiple components: positively affecting plant P stocks (likely via fertilizer inputs and stock calculations involving bulk density), Corg stocks, and microbial P stocks.
  • Management intensity reduced plant species richness; lower management intensity was associated with higher plant richness and lower plant P stocks (via lower biomass and conservative resource-use species).
  • A negative relationship emerged between plant P stocks and microbial P stocks, suggesting competition for P, potentially reinforced by less cooperative AMF species that retain P.
  • AMF abundance increased under lower management intensity and was positively associated with P exploitation. Reduced management increased both plant and AMF diversity/abundance, which together enhanced P exploitation. The combined indirect effect of management on P exploitation via AMF and plant species richness was −0.22, while the combined effect via Corg stocks was negligible (0.002).
  • Despite management modifying mechanisms, the positive relationship between plant species richness and P exploitation persisted in agricultural grasslands, but the underlying pathways differed from the experiment (more direct biodiversity links; missing mediators in the model suggested).
  • SEM fit was acceptable: Fisher’s C = 12.17, df = 12, p = 0.43.

• Application: Promoting above- and belowground biodiversity can tighten the P cycle in agricultural grasslands and potentially reduce fertilizer needs, offering ecological and economic benefits.

The findings show that higher plant diversity enhances the exploitation of soil P resources, but the mechanisms differ between controlled biodiversity plots and real-world agricultural grasslands. In the experiment, diversity-driven increases in soil C inputs fostered microbial growth and P uptake, which, together with higher plant biomass, raised organismic P stocks and tightened the P cycle. In agricultural systems, management intensity acted as a dominant driver, directly shaping soil Corg, plant P stocks, microbial P, and biodiversity. Nonetheless, reduced management intensity increased plant and AMF diversity, and AMF abundance was directly linked to higher P exploitation, demonstrating that both above- and belowground biodiversity contribute meaningfully to P cycling even when management exerts strong influence. These results bridge experimental BEF theory with agricultural practice, emphasizing that biodiversity can support nutrient-use efficiency and reduce reliance on external P inputs.

This study demonstrates that plant species richness and mycorrhizal biodiversity jointly enhance P exploitation in grasslands. In biodiversity experiments, effects are mediated through increased soil Corg and microbial P stocks and greater plant biomass; in agricultural grasslands, management modifies or overrides these pathways, yet reduced management intensity fosters biodiversity that still tightens the P cycle via AMF and plant richness effects. The work highlights a practical win-win: conserving biodiversity can improve P-use efficiency and reduce fertilizer needs. Future research should identify and quantify the specific microbial taxa and functional genes driving P mobilization and transfer in managed grasslands, and test management strategies that optimize biodiversity, yields, and nutrient retention over the long term.

• Fully crossed long-term manipulations of biodiversity and management are impractical; observational agricultural data entail confounding between management intensity and biodiversity.
• Some measurements differ in timing across years (e.g., soil P fractions in 2013 vs. other variables in 2014 in the experiment), though cross-year correlations were strong; temporal mismatches remain a caveat.
• Plant P stocks were estimated by multiplying May biomass by the number of cuts, likely overestimating absolute stocks but used for comparability between systems.
• Different analytical methods for plant P (NIRS, ICP-OES after digestion, XRF) required calibrations; residual method differences could introduce measurement uncertainty.
• AMF sequencing methods and years differed between datasets (454 vs. Illumina; experimental AMF sampled in 2010), potentially decoupling AMF metrics from contemporaneous soil/plant measurements.
• Stock calculations depend on bulk density, which varies with management (e.g., compaction), potentially biasing stock-based comparisons.
• In agricultural grasslands, the direct mechanisms linking plant richness to P exploitation were not fully captured in the SEM, indicating missing variables or processes (e.g., specific microbial functional traits/genes, root traits).