“Land use is reliably predicted by soil burst respiration profiles”

Studies examining soil respiration across land uses have been published for over 100 years, but few studies have examined how variability in rates of respiration during a test period could elucidate microbial responses to wet-dry events and connect them to land use. Our study examined the interplay between land uses (LU) and maximum potential soil respiration across Atlantic Canada using partitioned profiles of ‘burst’ respiration measurements. We found that Crop Agriculture respiration peaked in the first 24 h of the incubation, while Forest peaked during the second and third 24 h periods. Pasture respiration was intermediary, peaking in the first two 24 h periods. These specific proportional respiration intervals were crucial in successfully predicting Crop Agriculture, Forest and Pasture LUs using machine learning models, with accuracy equivalent to comprehensive soil physicochemical parameters. Wetland respiration profiles were inconsistent with minimal predictive power, likely due to high variability of mineral and carbon (C) content. While early (24 h) respiration was proportionally largest for managed soils, indicating the impact of management in increasing respiration, absolute total respiration over 72 h was largest for natural soils, indicating their likely larger putative contribution, per unit area, to the atmospheric C pool. This study points to a novel approach for assessing the effect of agricultural and natural management on soils, that recognizes consistent gradients in the functional status of soil C as directly linked to land use and land management. • Partitioning soil respiration into 24 hr periods reveals clear functional patterns. • Soil respiration profiles in Atlantic Canada are tied to land use, not ecoregion. • Machine learning models accurately discriminate natural from agricultural land use. • 24 h respiration does not correlate to the total respiration over 72 h.