Spatio-temporal characterization of microbial heat production on undisturbed soil samples combining infrared thermography and zymography

[Display omitted] •Spatio-temporal detection of hot spots and hot moments.•Intersection of high-resolution spatial soil properties to predict heat production.•Initial β-glucosidase activity determines heat production.•Soil moisture contents and soil organic carbon affect soil surface temperature.•k-Means clustering enables untangling microscale microbial dynamics. Passive infrared thermography (IRT) has already been applied in several approaches for high-resolution and non-contact imaging of microbial hot spots and hot moments on soil sample surfaces. The technique has only been used on homogenized disturbed samples to characterize the in-situ heterogeneity of heat development. In this study, undisturbed top- and subsoils samples from two forest sites were used in a substrate-induced approach to capture surface heat production using passive IRT with homogeneously applied glucose and water. The soil sample surface temperature was measured at 10-minute intervals and a spatial resolution of 0.17 mm per pixel. The soil samples were incubated for five days during passive IRT measurements under controlled ambient conditions with a relative air humidity >95% and constant ambient air temperature of 20 °C. Soil sample surface characterization was done by using active IRT for soil moisture approximation and surface structure, digital photography to estimate soil organic carbon (SOC) contents from soil color parameters, and zymography to get an indicator of initial microbial activity. In a first step, surface temperature dynamics were characterized using mathematical and geostatistical methods concerning microbial hot spots and hot moments. The characterization of hot moments was performed using a Gaussian curve fit. The spatial information of the hot spots was described using geostatistical semivariance. In a second step, a stepwise forward regression using sample surface properties was performed to find explanatory variables for surface heat production. Finally, a hierarchical k-means clustering was applied to the sequential thermal images and transferred to the other spatial datasets for deeper insights into small-scale variations in heat production and to also consider the temporal perspective. With an ANOVA combined with Tukey’s HSD post hoc test, significant differences between the cluster groups were calculated. This study showed that the temperature difference between averaged glucose- and water-treated sample surface temperature increased up to 0.2 K with a