Soil extracellular enzymes released by microorganisms break down organic matter and are crucial in regulating C, N and P cycling. Soil pH is known to influence enzyme activity, and is also a strong driver of microbial community composition; but little is known about how alterations in soil pH affect enzymatic activity and how this is mediated by microbial communities. To assess long term enzymatic adaptation to soil pH, we conducted enzyme assays at buffered pH levels (2.5 to 10, 0.5 interval) on two historically managed soils maintained at either pH 5 or 7 from the Rothamsted's Park Grass Long-term experiment ). The pH optima for a range of enzymes was found to differ between the two soils, the direction of the shift being toward the source soil pH, indicating the production of pH adapted isoenzymes by the soil microbial community. Soil bacterial and fungal communities determined by amplicon sequencing were found to be clearly distinct between pH 5 and soil pH 7 soils, possibly explaining differences in enzymatic responses. Furthermore, β-glucosidase sequences extracted from metagenomes revealed an increased abundance of Acidobacteria in the pH 5 soils, and increased abundance of Actinobacteria in pH 7 soils; these taxonomic shifts were more pronounced for enzymatic sequences when compared with a number of housekeeping gene sequences. Particularly for the Acidobacteria, this indicates that broad taxonomic groups at phylum level may possess enzymatic adaptations which underpin competitiveness in different pH soils. More generally our findings have implications for modelling the efficiency of different microbial enzymatic processes under changing environmental conditions; and future work is required to identify trade-offs with pH adaptations, which could result in different activity responses to other environmental perturbations.

High-throughput sequencing 16S rRNA gene surveys have enabled new insights into the diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances across landscapes. However, current analytical approaches are of limited use in formalizing syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterize taxonomy. To address this, we used sequences obtained from a large nationwide soil survey (GB Countryside Survey, henceforth CS) to create a comprehensive soil specific 16S reference database, with coupled ecological information derived from survey metadata. Specifically, we modeled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on both the shape of landscape scale pH-abundance responses, and pH optima (pH at which OTU abundance is maximal). We identify that most of the soil OTUs examined exhibited a non-flat relationship with soil pH. Further, the pH optima could not be generalized by broad taxonomy, highlighting the need for tools and databases synthesizing ecological traits at finer taxonomic resolution. We further demonstrate the utility of the database by testing against geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and accuracy in predicting pH responses of query sequences from a separate large soil survey. We found that the CS database provided good coverage of dominant taxa; and that the taxa indicating soil pH in a query dataset corresponded with the pH classifications of top matches in the CS database. Furthermore we were able to predict query dataset community structure, using predicted abundances of dominant taxa based on query soil pH data and the HOF models of matched CS database taxa. The database with associated HOF model outputs is released as an online portal for querying single sequences of interest (https://shiny-apps.ceh.ac.uk/ID-TaxER/), and flat files are made available for use in bioinformatic pipelines. The further development of advanced informatics infrastructures incorporating modeled ecological attributes along with new functional genomic information will likely facilitate large scale exploration and prediction of soil microbial functional biodiversity under current and future environmental change scenarios.

12High-throughput sequencing 16S rRNA gene surveys have enabled new insights into the 13 diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances 14 across landscapes. However, current analytical approaches are of limited use in formalising 15 syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are 16 typically unique to individual studies and sequence identification databases only characterise 17 taxonomy. To address this, we used sequences obtained from a large nationwide soil survey 18 (GB Countryside Survey, henceforth CS) to create a comprehensive soil specific 16S reference 19 database, with coupled ecological information derived from the survey metadata. Specifically, 20 we modelled taxon responses to soil pH at the OTU level using hierarchical logistic regression 21 (HOF) models, to provide information on putative landscape scale pH-abundance responses. 22 We identify that most of the soil OTUs examined exhibit predictable abundance responses 23 across soil pH gradients, though with the exception of known acidophilic lineages, the pH 24 optima of OTU relative abundance was variable and could not be generalised by broad 25 taxonomy. This highlights the need for tools and databases to predict ecological traits at finer 26 taxonomic resolution. We further demonstrate the utility of the database by testing against 27 geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and 28 accuracy in predicting pH responses of query sequences from a separate large soil survey. We 29 found that the CS database provided good coverage of dominant taxa; and that the taxa 30 indicating soil pH in a query dataset corresponded with the pH classifications of top matches 31 in the CS database. Furthermore we were able to predict query dataset community structure, 32 using predicted abundances of dominant taxa based on query soil pH data and the HOF models 33 of matched CS database taxa. The database with associated HOF model outputs is released as 34 an online portal for querying single sequences of interest (https://shiny-apps.ceh.ac.uk/ID-35 TaxER/), and flat files are made available for use in bioinformatic pipelines. The further 36 development of advanced informatics infrastructures incorporating modelled ecological 37 attributes along with new functional genomic information will likely facilitate large scale 38 exploration and prediction of soil microbial functional biodiversity under current and future 39 environmental change scenarios.40 41 42 Soil bacteria are highly diverse 1, 2 and are significant contributors to soil functionality. 43 Sequencing of 16S rRNA genes has enabled a wealth of new insights into the taxonomic 44 diversity of soil prokaryotic communities, revealing the ecological controls on a vast diversity 45 of yet to be cultured taxa with unknown functional potential 3 . However, despite thousands of 46 studies across the globe, we are still some way from synthesising the new knowledge on the ...