Deadwood forms a significant carbon pool in forest systems and is a potential source of dissolved organic carbon (DOC) input to soil, yet little is known about how deadwood effects forest soil carbon cycling. Deadwood DOC inputs to soil may be retained through sorption or may prime microbial decomposition of existing organic matter to produce additional DOC. To determine impacts of deadwood on soil C cycling, we analysed surface soil from beneath deadwood or leaf litter only, along chronosequences of stands of lowland oak and upland Sitka spruce. The concentration and quality (by optical indices) of water-extracted soil DOC (water-extractable organic carbon; WEOC), in situ decomposition ‘tea bag index’ (TBI) parameters and enzymatic potential assays (β-D-cellubiosidase, β-glucosidase, β-xylosidase, leucine aminopeptidase, phosphatase, phenol oxidase) were determined. Presence of deadwood significantly (p < 0.05) increased WEOC concentration (~ 1.5 to ~ 1.75 times) in the mineral oak soil but had no effect on WEOC in spruce soils, potentially because spruce deadwood DOC inputs were masked by a high background of WEOC (1168 mg kg⁻¹ soil) and/or were not retained through mineral sorption in the highly organic (~ 90% SOM) soil. TBI and enzyme evidence suggested that deadwood-derived DOC did not impact existing forest carbon pools via microbial priming, possibly due to the more humified/aromatic quality of DOC produced (humification index of 0.75 and 0.65 for deadwood and leaf litter WEOC, respectively). Forest carbon budgets, particularly those for mineral soils, may underestimate the quantity of DOC if derived from soil monitoring that does not include a deadwood component.

Rapid, low-cost methods for large-scale assessments of soil organic carbon (SOC) are essential for climate change mitigation. Our work explores the potential for citizen scientists to gather soil colour data as a cost-effective proxy of SOC instead of conventional lab analyses. The research took place during a 2-year period using topsoil data gathered by citizen scientists and scientists from urban parks in the UK and France. We evaluated the accuracy and consistency of colour identification by comparing “observed” Munsell soil colour estimates to “measured” colour derived from reflectance spectroscopy, and calibrated colour observations to ensure data robustness. Statistical relationships between carbon content obtained by loss on ignition (LOI) and (i) observed and (ii) measured soil colour were derived for SOC prediction using three colour components: hue, lightness, and chroma. Results demonstrate that although the spectrophotometer offers higher precision, there was a correlation between observed and measured colour for both scientists (R² = 0.42; R² = 0.26) and citizen scientists (R² = 0.39; R² = 0.19) for lightness and chroma, respectively. Foremost, a slightly stronger relationship was found for predicted SOC using the spectrophotometer (R² = 0.69), and citizen scientists produced comparable results (R² = 0.58), highlighting the potential of a large-scale citizen-based approach for SOC monitoring.