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Raw data gross Pi fluxes and soil analyses

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Version 2 2025-03-25, 16:51
Version 1 2025-03-10, 09:12
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posted on 2025-03-25, 16:51 authored by Viktor Van de VeldeViktor Van de Velde, Marijn BautersMarijn Bauters, Pascal Boeckx

Phosphorus (P) is crucial for ecosystem functioning, yet primary productivity in many tropical regrowth forests on highly weathered soils is assumed to be limited by P availability. Here, we used an isotope pool dilution (IPD) technique to quantify gross inorganic P (Pi) transformation rates along secondary forest succession trajectories in the central Congo Basin to assess land-use change effects on soil P cycling.

We considered gross Pi desorption and gross organic P (PO) mineralization together as a joint influx of Pi into the bicarbonate-extractable Pi pool (BIC-extractable Pi; PBIC), termed “gross mobilization”, while gross Pi sorption and gross microbial Pi uptake were treated as a joint efflux of Pi from the PBIC pool, referred to as “gross immobilization”. Average gross fluxes ranged between 0.11 and 0.26 µg P g-1 d-1, which is at the lower end of globally observed soil gross P cycling rates. No significant trends in soil BIC-extractable Pi or Pi fluxes were found along secondary forest succession trajectories, and PBIC showed a rapid turnover time (ca. 2 days). Environmental controls on gross Pi transformation rates varied between sites with the highest gross rates noted for sandy soils, while heavier, clayey soils resulted in lower rates, as well as lower PBIC. Significant predictors for gross Pi fluxes included variables related to microbial activity (tracer recovery in microbial biomass and microbial biomass carbon), as well as the PBIC pool size and dissolved organic carbon, reflecting both biotic and abiotic controls.

These findings highlight the importance of microbial and physicochemical characteristics for P cycling in tropical forests soils, especially in landscapes impacted by slash-and-burn agriculture. Furthermore, enhancing representation of soil P cycling in global dynamic vegetation models can improve predictions of resilience of tropical forest.

Funding

BOF.MET.2021.0004.01

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