DOI: 10.5061/dryad.4tmpg4fmh
title: Data from: Plant-soil feedback drives the nursing effect on Sitka spruce

description: The productivity of economically valuable focal trees in mixtures is often
 improved by inclusion of lower- value nursing species, but the mechanisms
 underpinning such effects are poorly resolved. This gap in understanding
 limits the capacity to develop efficient planting strategies for forests
 and woodlands to contribute to net-zero and other critical ecosystem
 functions. Here, we undertook a plant-soil feedback experiment to test the
 hypothesis that feedback effects improve the biomass of Sitka spruce
 (Picea sitchensis (Bong.) Carr) in soil conditioned by monocultures of
 heterospecific nurse species, and a mixture comprising Sitka spruce and
 heterospecifics, compared to soil conditioned by only Sitka spruce. Sitka
 spruce saplings had greater biomass in soil conditioned by Scots pine
 monocultures (Pinus sylvestris L.) and the mixture compared to their own
 soil or soil conditioned by silver birch (Betula pendula Roth).
 Statistical models showed that colonisation of ectomycorrhizal fungi on
 tree roots in the feedback phase was positively related to seedling
 biomass, and significantly influenced plant growth strategy. Soil
 inorganic nitrogen concentrations were strongly affected by monocultures
 and mixtures in the conditioning phase, but these effects were not related
 to biomass of seedlings in the feedback phase. However, the positive
 associations among microbial biomass nitrogen, extractable ammonium in
 soil, and the activity of the nitrogen-degrading enzyme
 N-acetylglucosaminidase may influence seedling biomass in the longer-term
 by stimulating nitrogen cycling. Synthesis and Applications: Our findings
 show that in the crucial early phases of tree growth, plant-soil feedback
 plays an important role in shaping productivity of Sitka spruce and the
 nitrogen cycle in forest soils, the latter which may have important
 consequences for tree biomass in the longer-term. Our findings demonstrate
 the importance of the nursing effect in the early stages of plant growth
 and provide a mechanistic explanation both for long-held observations of
 nursing effects in forestry systems and for biodiversity effects in
 natural woodlands. The nursing effect was largely driven by the activity
 of symbiotic ectomycorrhizal fungi, and therefore it is important for
 practitioners to ensure nursing species monocultures or mixtures support
 ectomycorrhizal fungi that are compatible with high-value target species.
description: # Plant-soil feedback drives the nursing effect on Sitka spruce
 [https://doi.org/10.5061/dryad.4tmpg4fmh](https://doi.org/10.5061/dryad.4tmpg4fmh) ## Description of the data and file structure Author: Yichen Zhou, University of Manchester Contact: [yichenzhou22yc@gmail.com](mailto:yichenzhou22yc@gmail.com) Date:2024/11/13 Contains material related to the paper: Plant-soil feedback drives the ‘nursing effect’ on Sitka spruce. Authors: Yichen Zhou, Tingting Tao, Filipa cox, David Johnson Description: This README file describes the model code associated with the above publication. All unites and methods are described in the publication. ### Files and variables #### File: Zhou\_et.al-data.xlsx **Description:** We have compiled all the data into “Zhou et.al-data”, which contains different sheets, including "Plant properties“ which includes the plant properties after the feedback phase, ”Condition soil“ which includes the soil properties after the conditioning phase, "PSF" which includes the PSF values, and " ECM-bio " which include the binomial distribution data of ECM colonization at each root tip. Additionally, we placed the soil properties of Sitka spruce, Scots pine, and silver birch after the feedback phase into three separate sheets ("Feedback soil - spruce", "Feedback soil - pine", and "Feedback soil - birch"). Cells with 'null' represent the value that is missing, since we only measured the PLFA of 8 replicates. The microbial biomass-specific enzyme activity calculations were based on PLFA value, so they are also missing. ##### Variables | Column names | Definition | Unit | | :------------------------ | :---------------------------------------------------------------------------- | :------------------------- | | Aboveground biomass | Total biomass of leaf and stem | g | | Above:underground biomass | The ratio of aboveground biomass and underground biomass | NA | | Bacteria | Bacterial PLFA | nmol g-1 | | BI | Branching intensity | tips cm-1 | | Condition species | The seeding species in the conditioning phase | NA | | Feedback species | The seeding species in the feedback phase | NA | | Fungi:bacteria | The ratio of fungi PLFA and bacteria PLFA | NA | | Fungi PLFA | Fungal PLFA | nmol g-1 | | ECM | Ectomycorrhizal colonisation | % | | GLC | β-glucosidase | nmol prod. nmol-1 PLFA h-1 | | Gram- bacteria | Gram-negative bacteria PLFA | (nmol g-1) | | Gram+ bacteria | Gram-positive bacteria PLFA | (nmol g-1) | | Gram+ :Gram- | The ratio of Gram-positive bacteria PLFA and Gram-negative bacteria PLFA | NA | | LDMC | Leaf dry matter content | mg g-1 | | MBC | Microbial biomass C | ug g-1 | | MBN | Microbial biomass N | ug g-1 | | Inorganic N | Inorganic nitrogen | ug g-1 | | Inorganic P | Inorganic phosphorus | ug g-1 | | NH4 | Ammonia nitrogen | ug g-1 | | NO3 | Nitrate nitrogen | ug g-1 | | NAG | N-acetylglucosaminidase | nmol prod. nmol-1 PLFA h-1 | | PHO | Phosphatase | nmol prod. nmol-1 PLFA h-1 | | Photosysnthetic rate | Photosysnthetic rate | umol m-2s-1 | | PLFA | Phospholipid fatty acid | nmol g-1 | | PSF | Plant-soil feedback | NA | | Replicate | Data from which replicate group | NA | | RTD | Root tissue density | g cm-3 | | Root diameter | Root diameter | mm | | rootsystem | The root system from the feedback species | NA | | Seedling | Condition species with the replicate group they come from | NA | | SLA | Specific leaf area | m2 kg-1 | | SRL | Specific root length | m g-1 | | Total PLFA | The sum of all PLFA | nmol g-1 | | Total biomass | Total plant biomass | g | | Total bacteria | The sum of Gram-positive bacteria, Gram-negative bacteria, and bacterial PLFA | nmol g-1 | | Underground biomass | Root biomass | g | ## Code/software All statistical tests were performed in R v.4.2.0. Please find the code in the file "Zhou et al. - R-code." It contains the code for the following analyses: linear model, Generalized Linear Model (GLM), correlation analyses, Principal Component Analysis (PCA), and Redundancy Analysis (RDA).