Combining automated chambers with surveys to measure spatial and temporal variation in soil carbon, soil respiration and net ecosystem exchange in four contrasting ecosystems.

Lee, Mark; Egan, Gary

Combining automated chambers with surveys to measure spatial and temporal variation in soil carbon, soil respiration and net ecosystem exchange in four contrasting ecosystems.

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EGU25-20183-print.pdf

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Lee, Mark

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Egan, Gary

Date published

March 15, 2025

Abstract

An estimated 80% of terrestrial carbon is stored below-ground but a large proportion of our soils are carbon depleted. There are broadly two ways to increase organic carbon stored below-ground, either protect and expand carbon-rich ecosystems or manage ecosystems to enhance carbon sequestration. Identifying carbon-rich ecosystems and understanding the key drivers of carbon losses is essential to design ecosystem management. However, the key processes which drive carbon losses are multifactorial and can vary between ecosystems and locations. We present a two-year study of four contrasting ecosystems situated in proximity. Our focal ecosystems were (a) an unimproved species-rich grassland, (b) managed hazel coppice woodland, (c) broadleaved woodland, and (d) coniferous woodland. We monitored temporal variability in soil respiration (SR) and net ecosystem exchange (NEE) using automated chambers and we measured spatial variability in SR and NEE across the ecosystems on a quarterly basis using survey chambers, also measuring a suite of biogeochemical, and plant and soil biodiversity metrics. We found that mean soil carbon was greatest in the broadleaved woodland, then the coniferous woodland, followed by the hazel coppice and finally the grassland site. Interestingly, soil carbon and many of the other biogeochemical parameters varied throughout the year and across the ecosystems. Soil moisture and soil temperature were important drivers of changes in SR and NEE, but the magnitude of these effects varied between the ecosystems and over time. Spatial variability in SR and NEE was also substantial with many of the biogeochemical and biodiversity metrics each explaining some of the variation in our data. Overall, high-resolution temporal datasets from automated chambers combined with spatial data using survey chambers in different ecosystems located closely together gives us a greater understanding of the key drivers of changes in SR and NEE. Using these data could inform land management decisions aimed at increased soil carbon sequestration. This could make an important contribution to achieving net zero.