The Leaf-Wax Carbon Isotopes in the Urban World: Revisiting Climate-Isotope Relation Under Changing Land-Covers

Anthropogenically induced global land cover (LC) change has modulated the carbon and moisture budget at the ecosystem level, impacting the holy trinity of climate change i.e. atmosphere-hydrosphere-biosphere. Plants (native to different LC types) have the ability to modify their biogeochemical response to a changing environment and therefore can be used to quantify the patterns and drivers of LC change. While the compound-specific changes in plants (such as n-alkane, n-alkanoic acid) are widely studied, spatially explicit and thematically detailed quantitative analyses of LC-driven changes in biogeochemical properties of terrestrial plants are mostly lacking. To this end, the carbon isotopic signal in C3 plant leaves (13Cn-alkane) have been used as an index for characterizing the spatiotemporal effects of LC and associated climatic change on plant carbon isotopic fractionation mechanisms. We propose a Machine Learning-powered polynomial regressor founded on climatological (satellite-derived) and biogeochemical (GIS-modelled) input variables, which mimics a plant types' environment at any spacetime coordinate, simulating 13Cn-alkane values. The model is constrained on the premise that a C3 plants 13Cn-alkane value is a function of its climate (precipitation, temperature, pCO2), geography (latitude, longitude and altitude) and ecosystem (LC, productivity and soil moisture content). Our model can explain ca. 43% of the total variations recorded at the species level, where the predictors in decreasing order of their magnitudes are: LC, temperature, latitude, productivity and precipitation. Longitude, pCO2, and soil moisture content were found to exert only a minor influence. The models ability to capture the effects of the forcing parameters reduces for certain locations which are highly perturbed by human activities (LC defined as cropland and urban), indicating anthropogenic impact can overpower natural effects on plant biogeochemistry. Our model enables the evaluation of climatic and geographic effects for both pristine and perturbed LC types, facilitating a more comprehensive application of plant isotopes in ecosystem characterization studies.