Evidence for functional state transitions in intensively-managed soil ecosystems.

Soils are fundamental to terrestrial ecosystem functioning and food security, thus their resilience to disturbances is critical. Furthermore, they provide effective models of complex natural systems to explore resilience concepts over experimentally-tractable short timescales. We studied soils derived from experimental plots with different land-use histories of long-term grass, arable and fallow to determine whether regimes of extreme drying and re-wetting would tip the systems into alternative stable states, contingent on their historical management. Prior to disturbance, grass and arable soils produced similar respiration responses when processing an introduced complex carbon substrate. A distinct respiration response from fallow soil here indicated a different prior functional state. Initial dry:wet disturbances reduced the respiration in all soils, suggesting that the microbial community was perturbed such that its function was impaired. After 12 drying and rewetting cycles, despite the extreme disturbance regime, soil from the grass plots, and those that had recently been grass, adapted and returned to their prior functional state. Arable soils were less resilient and shifted towards a functional state more similar to that of the fallow soil. Hence repeated stresses can apparently induce persistent shifts in functional states in soils, which are influenced by management history.

Distinct respiratory responses of soils to complex organic substrate are governed predominantly by soil architecture and its microbial community.

Factors governing the turnover of organic matter (OM) added to soils, including substrate quality, climate, environment and biology, are well known, but their relative importance has been difficult to ascertain due to the interconnected nature of the soil system. This has made their inclusion in mechanistic models of OM turnover or nutrient cycling difficult despite the potential power of these models to unravel complex interactions. Using high temporal-resolution respirometery (6 min measurement intervals), we monitored the respiratory response of 67 soils sampled from across England and Wales over a 5 day period following the addition of a complex organic substrate (green barley powder). Four respiratory response archetypes were observed, characterised by different rates of respiration as well as different time-dependent patterns. We also found that it was possible to predict, with 95% accuracy, which type of respiratory behaviour a soil would exhibit based on certain physical and chemical soil properties combined with the size and phenotypic structure of the microbial community. Bulk density, microbial biomass carbon, water holding capacity and microbial community phenotype were identified as the four most important factors in predicting the soils' respiratory responses using a Bayesian belief network. These results show that the size and constitution of the microbial community are as important as physico-chemical properties of a soil in governing the respiratory response to OM addition. Such a combination suggests that the 'architecture' of the soil, i.e. the integration of the spatial organisation of the environment and the interactions between the communities living and functioning within the pore networks, is fundamentally important in regulating such processes.

On the origin of carbon dioxide released from rewetted soils.

When dry soils are rewetted a pulse of CO2 is invariably released, and whilst this phenomenon has been studied for decades, the precise origins of this CO2 remain obscure. We postulate that it could be of chemical (i.e. via abiotic pathways), biochemical (via free enzymes) or biological (via intact cells) origin. To elucidate the relative contributions of the pathways, dry soils were either sterilised (double autoclaving) or treated with solutions of inhibitors (15% trichloroacetic acid or 1% silver nitrate) targeting the different modes. The rapidity of CO2 release from the soils after the drying:rewetting (DRW) cycle was remarkable, with maximal rates of evolution within 6 min, and 41% of the total efflux over 96 h released within the first 24 h. The complete cessation of CO2 eflux following sterilisation showed there was no abiotic (dissolution of carbonates) contribution to the CO2 release on rewetting, and clear evidence for an organismal or biochemical basis to the flush. Rehydration in the presence of inhibitors indicated that there were approximately equal contributions from biochemical (outside membranes) and organismal (inside membranes) sources within the first 24 h after rewetting. This suggests that some of the flux was derived from microbial respiration, whilst the remainder was a consequence of enzyme activity, possibly through remnant respiratory pathways in the debris of dead cells.

Defining and quantifying the resilience of responses to disturbance: a conceptual and modelling approach from soil science.

There are several conceptual definitions of resilience pertaining to environmental systems and, even if resilience is clearly defined in a particular context, it is challenging to quantify. We identify four characteristics of the response of a system function to disturbance that relate to "resilience": (1) degree of return of the function to a reference level; (2) time taken to reach a new quasi-stable state; (3) rate (i.e. gradient) at which the function reaches the new state; (4) cumulative magnitude of the function (i.e. area under the curve) before a new state is reached. We develop metrics to quantify these characteristics based on an analogy with a mechanical spring and damper system. Using the example of the response of a soil function (respiration) to disturbance, we demonstrate that these metrics effectively discriminate key features of the dynamic response. Although any one of these characteristics could define resilience, each may lead to different insights and conclusions. The salient properties of a resilient response must thus be identified for different contexts. Because the temporal resolution of data affects the accurate determination of these metrics, we recommend that at least twelve measurements are made over the temporal range for which the response is expected.