Aims The importance of quantifying carbon stocks in terrestrial ecosystems is crucial for determining climate change dynamics. However, the present regional assessments of carbon stocks in tropical grasslands are extrapolated to unsampled areas with a high degree of uncertainty and without considering the carbon and nitrogen composition of vegetation and soil along altitudinal ranges. This study aims to assess carbon and nitrogen concentrations in soil and vegetation, aboveground carbon stocks distribution and soil organic carbon stocks along an altitudinal range in the páramo region in the Ecuadorian Andes. Methods The vegetation inventory was conducted using 15×15 m sampling plots distributed in three altitudinal ranges. Based on the patterns exhibited by the dominant vegetation growth forms, biomass and soil were sampled to quantify the corresponding carbon and nitrogen concentrations. Subsequently, the aboveground live biomass along the páramo altitudinal range was estimated using allometric equations. Finally, soil and vegetation carbon stocks were estimated for the entire basin. Important Findings Altitudinal analysis supported a potential distribution of carbon and nitrogen concentrations in soil, litter and live tissues, where higher concentrations were found in the low altitudinal range mainly for tussocks and acaulescent rosettes. Cellulose in litter showed higher concentrations at low altitudinal ranges for acaulescent rosettes and cushions only. For the same growth forms, lignin patterns in litter were higher in high altitudinal ranges. Soil texture provided complementary information: high percentage of silt was highly correlated to high soil nitrogen and carbon concentration. Tussocks were found to be responsive to altitude with their, highest aboveground carbon stocks occurring at the low altitudinal range, but cushions and acaulescent rosettes responded differently. The established relationships among soil, vegetation and altitude shown in this study must be taken into account to estimate both aboveground and soil organic carbon stocks in páramo regions - such estimates will be considerably inaccurate if these relationships are ignored.

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Many terrestrial biogeochemistry process models have been applied around the world at different scales and for a large range of ecosystems. Despite being essential ecosystems that sustain important ecological processes, only a few efforts have been made to estimate the gross primary production (GPP) and the hydrological budgets along an altitudinal gradient for grasslands in the Andean Region. One of the few previous studies in the region considered the heterogeneity of the main properties of the páramo vegetation and showed significant differences in plant functional types, site/soil parameters, and daily meteorology. This study extends the work previously mentioned by using the Biome-BGC model to simulate the GPP and the water fluxes in a representative area of the Ecuadorian Andean páramos. It focuses on three main growth forms of vegetation and is also extended to cells with similar properties. The responses of GPP and the water fluxes were dependent on environmental drivers, ecophysiology, and site-specific parameters. The results showed that the GPP estimations at lower elevations are more than twice the estimations at higher elevations, which might have a large implication during extrapolations at larger spatiotemporal scales. The assessment of the water fluxes in the páramo ecosystem was inaccurate, presumably due to the poor estimation of the soil processes, water storage, and evaporative processes. A further development in the soil and evaporative modeling process of Biome-BGC is needed in order to be fully applicable in the high-altitudinal páramo ecosystems. An accurate estimation of the temporal changes of carbon and water budgets can potentially assess the effect of the climate drivers in the biomass productivity of this terrestrial ecosystem.@en

In the Andes mountainous region of South America grasslands known as páramos provide important ecosystem services like sustaining biodiversity, securing carbon sequestration and providing water storage. However, many páramos regions are subject to land use change due to expanding agriculture, intensified grazing and land burning. These are usually caused by socio-economic factors driving local communities to increase their income generation. Trying to achieve a better understanding of the páramos is often restricted to exploring specific details and does not follow an integrated approach or a comprehensive ecosystem analysis. In this research the focus is on better understanding the dominant ecohydrological processes and their interactions. An integrated approach is followed using in-situ measurements, field experiments, laboratory analyses, and numerical modelling. Also, different hydroinformatics tools are used to identify and quantify the ecosystem services provided by the páramos. Moreover, a framework is developed that allows a more realistic quantification and mapping of the main ecosystem services. The approach was carried out for a test site in an Ecological area in North Ecuador. The findings show a clear difference in ecosystem services depending on their altitudinal range and type of vegetation. These results can be used to further develop environmental management and landscape planning strategies, in order to better meet the social goals. This research is aligned with the priorities advocated in the IPCC Report (2007) ‘to improve representation of the interactive coupling between ecosystems and the climate system’, and with SDG #15: Life on Land ‘By 2020, ensure the conservation, restoration and sustainable use of terrestrial and inland freshwater ecosystems and their services’.@en

As one of the main areas of carbon cycle and climate change studies, water and CO2 relations are of great significance for estimation of gross primary production (GPP). Various biogeochemical process-based models have been set up to estimate the GPP based on mathematical representation of biological, physiological and ecological processes. However, they ended up increasing the complexity and computational processing power due to the large number of physical equations that need to be solved. Computational time becomes an important matter in the simulation of multiple scenarios using models for long periods of time (e.g. climate projections). Data driven surrogate models have proven to be a useful tool for environmental modelling especially when ecological and climatic co-variates are large. The advantages of Data Driven Models (DDM) are: the possibility of adding new independent variables even if their understanding is weak, and short computational time to run. The aim is to explore the ability of DDMs to replicate a biochemical model calculating GPP. This study evaluates the performance of four surrogate DDMs, namely linear regression method (LRM), model tree (MT), instance-based learning (IBL) and artificial neural network (ANN). A simple empirical and semi-empirical relationship between GPP and climatic variables are studied. Input variable selection (IVS) methods were used to decide on the most relevant and potential environmental model inputs and then followed by a two-step approach which included a model-free and a model-based technique. Data from the highlands (páramo ecosystem) in the Ecuadorian Andean Region from 12-year time-series (2000-2011) were used to evaluate the models at various time frames and at different altitudes. The GPP time series data for the same period were derived from an earlier study using the biomodel BIOME-BGC (BioGeochemical Cycles), which is a comprehensive physical based model used in different analysis of carbon fluxes around the world. So-called IBL (nearest neighbour method) showed a great capability to reproduce the GPP when data was aggregated to monthly time frame. The computational time used to evaluate the time series with IBL as the selected DDM is shorter with enough accuracy for using it in multi-model runs.@en