The continuum hypothesis states that both deterministic and stochastic processes contribute to the assembly of ecological communities. However, the contextual dependency of these processes remains an open question that imposes strong limitations on predictions of community responses to environmental change. Here we measure community and habitat turnover across multiple vertical soil horizons at 183 sites across Scotland for bacteria and fungi, both dominant and functionally vital components of all soils but which differ substantially in their growth habit and dispersal capability. We find that habitat turnover is the primary driver of bacterial community turnover in general, although its importance decreases with increasing isolation and disturbance. Fungal communities, however, exhibit a highly stochastic assembly process, both neutral and non-neutral in nature, largely independent of disturbance. These findings suggest that increased focus on dispersal limitation and biotic interactions are necessary to manage and conserve the key ecosystem services provided by these assemblages.

Understanding the influence of long-term crop management practices on the soil microbial community is critical for linking soil microbial flora with ecosystem processes such as those involved in soil carbon cycling. In this study, pyrosequencing and a functional gene array (GeoChip 4.0) were used to investigate the shifts in microbial composition and functional gene structure in a medium clay soil subjected to various cropping regimes. Pyrosequencing analysis showed that the community structure (βdiversity) for bacteria and fungi was significantly impacted among different cropping treatments. Functional gene array-based analysis revealed that crop rotation practices changed the structure and abundance of genes involved in C degradation. Significant correlations were observed between the activities of four enzymes involved in soil C degradation and the abundance of genes responsible for the production of respective enzymes, suggesting that a shift in the microbial community may influence soil C dynamics. We further integrated physical, chemical, and molecular techniques (qPCR) to assess relationships between soil C, microbial derived enzymes and soil bacterial community structure at the soil micro-environmental scale (e.g. within different aggregate-size fractions). We observed a dominance of different bacterial phyla within soil microenvironments which was correlated with the amount of C in the soil aggregates suggesting that each aggregate represents a different ecological niche for microbial colonization. Significant effects of aggregate size were found for the activity of enzymes involved in C degradation suggesting that aggregate size distribution influenced C availability. The influence of cropping regimes on microbial and soil C responses declined with decreasing size of soil aggregates and especially with silt and clay micro-aggregates. Our results suggest that long term crop management practices influence the structural and functional potential of soil microbial communities and the impact of crop rotations on soil C turnover varies between different sized soil aggregates. These findings provide a strong framework to determine the impact of management practices on soil C and soil health.

Our findings indicate the importance of paleoclimatic information to improve quantitative predictions of global soil C stocks.

No abstract

The carbon accounting model FullCAM is used in Australia's National Greenhous Gas Inventory to provide estimates of carbon stock changes and emissions in response to deforestation and afforestation / reforestation. FullCAM-predicted above-ground woody biomass is heavily influenced by the parameter M, which defines the maximum upper limit to biomass accumulation for any location within the Australian continent. In this study we update FullCAM's M spatial input layer through combining an extensive database of 5,739 site-based records of above-ground biomass (AGB) with the Random Forest ensemble machine learning algorithm, with model predictions of AGB based on 23 environmental predictor covariates. A Monte-Carlo approach was used, allowing estimates of uncertainty to be calculated. Overall, the new biomass predictions for woodlands, with 20-50% canopy cover, were on average 49.5±1.3 (s.d.) t DM ha-1 , and very similar to existing model predictions of 48.5 t DM ha-1. This validates the original FullCAM model calibrations, which had a particular focus on accounting for greenhouse gas emissions in Australian woodlands. In contrast, the prediction of biomass of forests with a canopy cover >50% increased significantly, from 172.1 t DM ha-1 , to 234.4±5.1 t DM ha-1. The change in forest biomass was most pronounced at sub-continental scales, with the largest increases in the states of Tasmania (166 to 351±22 t DM ha-1), Victoria (201 to 333±14 t DM ha-1), New South Wales (210 to 287±9 t DM ha-1), and Western Australia (103 to 264±14 s.d. t DM ha-1). Testing of model predictions against independent data from the savanna woodlands of northern Australia, and from the high biomass Eucalyptus regnans forests of Victoria, provided confidence in the predictions across a wide range of forest types and standing biomass. When applied to the Australian Government's National Inventory land clearing accounts there was an overall increase of 6% in continental emissions over the period 1970-2016. Greater changes were seen at sub-continental scales calculated within 6° x 4° analysis tiles, with differences in emissions varying from-21% to +35%. Further testing is required to assess the impacts on other land management activities covered by the National Inventory, such as reforestation; and at more local scales for sequestration projects that utilise FullCAM for determining abatement credits.