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

Abstract

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.

Keywords: Bayesian belief network; Complex substrate; Microbial community; Soil architecture; Soil respiration.

Fig. 1

Four components of the descriptive model fitted to the experimental data. Top panel shows the initial decay (purple – Eq. (2)) containing information about B – the amplitude of the peak, and k – the decay rate alongside the slow decay function (green) which was fixed at 0.1 – Eq. (5). Lower panel shows the components describing the secondary (red – Eq. (3)) and tertiary (blue – Eq. (4)) peaks containing information about the time to peaks (τ₂ and τ₃), amplitude of peaks (A₂ and A₃), and the area under each curve (Area₂ and Area₃). The black line is the same in each panel and shows the summation of all 4 parts to give the descriptive model as a whole (Eq. (1)). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Representative examples of the observed (crosses) and modelled (lines) respiration profiles after the addition of the substrate to diverse soils. The example soils are (a) Soil 33; (b) Soil 57; (c) Soil 66; (d) Soil 17; (e) Soil 63; (f) Soil 23; (g) Soil 56; (h) Soil 52, full details about all soils can be found in Table S1’.

Fig. 3

Results of hierarchical clustering of the model parameters listed section 2.5. showing 4 archetypical respiratory responses to the addition of organic matter. Laboratory replicates show a high degree of reproducibility within one site as is seen in the example plot on the left hand side where each plot is 1 site and n = 6.

Fig. 4

Principal components analysis used to confirm the clustering of sites shown in Fig. 3. The first 3 components account for 75% of the variation; black arrows show the loadings of the model parameters, whiskers = ± 1 SE.

Fig. 5

Panels show the results of a Bayesian belief network used to investigate the predictability of respiratory Types from knowledge of soil properties covering physical, chemical, biological, and management aspects of each site (a, b, c, and d show probabilities for Types 1, 2, 3, and 4 respectively). Values of the measured properties were split into 10 bins (where Bin 1 has the lowest values and 10 the highest) as defined for this dataset. For details of bin ranges see Table 2; the more intense the colour the more likely this value is for this type of respiratory behaviour, hatched area indicates empty cells with no value. Soil properties are shown in rank order of importance for predicting Type. BD - bulk density, MBC – microbial biomass carbon, WHC – water holding capacity, PC1, PC2, PC3 – phenotypic principal components, LU – land use, and RSG – representative soil group.