Natural and anthropogenic disturbances act as important drivers of tree mortality, shaping the structure, composition and biomass distribution of forests. Disturbance regimes may emerge from different characteristics of disturbance events over time and space. We design a model- based experiment to investigate the links between disturbance regimes at the landscape scale and spatial features of biomass patterns. The effects on biomass of a wide range of disturbance regimes are simulated by varying three different parameters, i.e. μ (probability scale), α (clustering degree), and β (intensity slope) that shape the extent, frequency, and intensity of disturbance events, respectively. A simple dynamic carbon cycle model is used to simulate 200 years of plant biomass dynamics in response to circa +2000 different disturbance regimes, depending on the different combinations of μ, α, and β. Each parameter combination yields a spatially explicit estimate of plant biomass for which sixteen synthesis statistics are estimated on the spatial distributions of biomass, including information-based and texture features. Based on a multi-output regression approach we link these synthesis statistics with additional gross primary production (GPP) constraints to retrieve the three disturbance parameters. In doing so we evaluate the confidence in inferring disturbance regimes from spatial distributions of biomass. Our results show that all three parameters can be confidently retrieved. The Nash-Sutcliffe efficiency for the prediction of the μ, α, and β is 97.3%, 96.6%, and 97.9%, respectively. A feature importance analysis reveals that the distribution statistics dominate the prediction of μ and β, while features quantifying texture have a stronger connection with α. Overall, this study clarifies the association between biomass patterns emerging from different underlying disturbance regimes, while overcoming the previously found equifinality between mortality rates and total biomass. Given the links between decadal vegetation dynamics and the uncertainties in the role of terrestrial ecosystems in the global biogeochemical cycles, a better understanding and the quantification of disturbance regimes would improve our current understanding of controls and feedback at the biosphere-atmosphere interface in the current Earth system models.

The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO₂ fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO₂ fertilization effect from other long-term drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO₂ fertilization effect on GPP and daily maximum net ecosystem production (NEPmax) using eddy covariance records across 38 extratropical forest sites. We find the median stimulation rate of GPP and NEPmax to be 16.4 ± 4% and 17.2 ± 4% per 100 ppm increase in atmospheric CO₂ across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process-based land surface models. We acknowledge that additional factors, including nitrogen deposition and land management, may impact plant productivity, potentially confounding the attribution to the CO₂ fertilization effect. Assuming these site-specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO₂ fertilization effect. However, disentanglement of these long-term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long-term CO₂ fertilization across multiple eddy covariance sites. Such observation-based quantification is key to constraining the long-standing uncertainties in the land carbon cycle under rising CO₂ concentrations.