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DISTRIBUTED LAND USE CHANGE EFFECTS ON SOIL BIODIVERSITY AND FUNCTION

Taxonomic indicators

H. Background Key findings Outputs New science is required to better understand the impacts of land use intensification on biodiversity & key soil processes such as carbon sequestration.

U-GRASS Approach

Long term experiments

“Real world” local contrasts

A) Quantification of change in biodiversity and function due to management across landscape. Locally paired management intensification survey (pristine grass v intensive), landscape scale soil survey (Countryside survey) & ecological management trials with industry partners

B) Lab experiments. Mechanistic understanding of soil carbon stabilisation mechanisms under different land uses & determining specific role of soil microbes in soil processes.

C) Measurements

Isotope labelling Soil inoculation experiments

- Soil biodiversity (Bacteria, Fungi, wider eukaryotes ) - Soil properties (C,N, P, pH, texture, LOI, moisture, FT-IR) - Soil process (enzyme assays, SIR, Carbon use efficiency - CUE) - Soil functional genes (QPCR N cycling genes, whole genome metagenomics)

Intensification effects on soil functional genes Soil Organic Matter

Denitrification (GHG release ?)

Anaerobic process (C storage?)

Grassland

Intensive Grassland

Arable

Degraded Arable

Climate/geography

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Metagenomic sequencing of grassland v arable / intensive grass contrasts reveals: 1) homogenisation of functional biodiversity with intensification; 2) key soil processes affected by management; & 3) identification of wider functions affected by change in SOM.

Intensification effects on carbon cycling processes differ across soil gradients

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Intensification typically reduces C, and increases soil pH.

Carbon use efficiency, an indicator of C storage, is variably affected by intensification according to the geo-climatic nature of the soil, and the effect of intensification on soil properties:

In mesic/neutral soils intensification reduces CUE

Intensification of acid soils across a critical pH threshold increases CUE Malik et al, Nature Communications, 2018

Mesic soils Acid soils

Indicators of intensification differ in response across soil gradients

pH 5 pH 7

National scale assessment of soil bacterial indicators of intensification: Different indicators are observed dependent on geo-climatic context.

Enzyme responses to intensification differ according to native soil pH (Rothamsted Park grass experiment

Functional indicators

Models and tools

Portal for sequence ID –built on predictive models of molecular taxa abundances

Landscape scale synthesis & prediction of molecular indicators

https://shiny-apps.ceh.ac.uk/ID-TaxER/

Process models

Changing the pH modification factor in ECOSSE to reflected observed biodiversity/functional thresholds.

Spatial models: Geo-climatic definition of soil state, and sensitivity to change

1. Land use identified as best predictor of soil biotic and functional state, followed by climate.

2. Identifying predictive thresholds provides context for defining soil normal operating ranges & predicting state change

3. Future soil change will largely depend on human land use and how it adapts to climate

Biotic Indicators for land managers & policy

Random forest prediction of soil state

Identifying climatic thresholds

Coupled climate driven models for land use & soil state

Specific bacterial taxa are the best indicators of SOM improvement in restoring calcareous grasslands

Minimum tillage and cover crops promote beneficial bacterial taxa and reduce putative pathogens

Key Messages Land use intensification typically reduces C in top 15cm soil and increases soil pH .

Within a local land use contrast, less intensive soils with more C have greater activity. However, this does not mean all functions (genes/enzymes) are enhanced

Soil biodiversity and functional responses to intensification are dependent on the “native” unimproved state of the soil (determined by the geo-climatic situation and identified by geospatial modelling). Intensification homogenises the natural variance of soil functions and biodiversity.

U-GRASS has generated large datasets describing the components of biodiversity and functions affected by land use, and identifies relevant bioindicators for use in future monitoring.

Policymakers and land managers need better indicators of soil health, which are applicable across different soil systems and scales.

The U-GRASS Consortia: Rob Griffiths, Kelly Mason, Jeremy Puissant, Tim Goodall, Jodey Peyton, Richard Pywell, Jeanette Whitaker, Niall McNamara, Briony Jones, Melanie Armbruster, Ashish Malik (CEH); Nick Ostle, Kate Buckeridge (UL); Pete Smith, Matthias Kuhnert (UA), Tom Bell, Maaike van Agtmaal (ICL); Aimeric Blaud, Penny Hirsch, Ian Clark (RR).

Contact: Rob Griffiths rig@ceh.ac.uk Twitter: @SSP_UGRASS