A combined approach to establishing the timing and magnitude of anthropogenic nutrient alteration in a mediterranean coastal lake- watershed system.
Magdalena FuentealbaClaudio LatorreMatías Frugone-ÁlvarezPablo SarricoleaSantiago GiraltManuel Contreras-LopezRicardo PregoPatricia BernárdezBlas L Valero-GarcésPublished in: Scientific reports (2020)
Human activities have profoundly altered the global nutrient cycle through Land Use and Cover Changes (LUCCs) since the industrial revolution and especially during the Great Acceleration (1950 CE). Yet, the impact of such activities on terrestrial and aquatic ecosystems above their ecological baselines are not well known, especially when considering the response of these systems to the intensity of LUCCs on nutrient cycles. Here, we used a multiproxy approach (sedimentological, geochemical and isotopic analyses, historical records, climate data, and satellite images) to evaluate the role that LUCCs have on Nitrogen (N) cycling in a coastal mediterranean watershed system of central Chile over the last two centuries. Despite long-term anthropogenic use (agriculture, cattle grazing) in the Matanzas watershed- lake system, these LUCC appear to have had little impact on nutrient and organic matter transfer since the Spanish Colonial period. In contrast, the largest changes in N dynamics occurred in the mid-1970s, driven by the replacement of native forests and grasslands by government-subsidized tree plantations of introduced Monterey pine (Pinus radiata) and eucalyptus (Eucalyptus globulus). These LUCC had major impacts on the transfer of organic matter (which increased by 9.4%) and nutrients (as revealed by an increase in total N) to Laguna Matanzas. Our study shows that the presence of anthropogenic land use/cover changes do not necessarily alter nutrient supply and N availability per se but rather it is the magnitude and intensity of such changes that produce major impact on these processes in these mediterranean watersheds.
Keyphrases
- climate change
- organic matter
- heavy metals
- water quality
- human health
- high intensity
- risk assessment
- magnetic resonance
- endothelial cells
- deep learning
- optical coherence tomography
- wastewater treatment
- machine learning
- electronic health record
- convolutional neural network
- artificial intelligence
- data analysis
- energy transfer
- quantum dots
- pluripotent stem cells