Carbon dioxide fluxes in Alpine grasslands at the Nivolet Plain, Gran Paradiso National Park, Italy 2017–2023

The version of record of this article, first published in [Scientific Data], is available online at Publisher’s website: http://dx.doi.org/10.1038/s41597-024-03374-1We introduce a georeferenced dataset of Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER) and meteo-climatic variables (air and soil temperature, air relative humidity, soil volumetric water content, pressure, and solar irradiance) collected at the Nivolet Plain in Gran Paradiso National Park (GPNP), western Italian Alps, from 2017 to 2023. NEE and ER are derived by measuring the temporal variation of CO2 concentration obtained by the enclosed chamber method. We used a customised portable non-steady-state dynamic flux chamber, paired with an InfraRed Gas Analyser (IRGA) and a portable weather station, measuring CO2 fluxes at a number of points (around 20 per site and per day) within five different sites during the snow-free season (June to October). Sites are located within the same hydrological basin and have different geological substrates: carbonate rocks (site CARB), gneiss (GNE), glacial deposits (GLA, EC), alluvial sediments (AL). This dataset provides relevant and often missing information on high-altitude mountain ecosystems and enables new comparisons with other similar sites, modelling developments and validation of remote sensing data.This work was funded by the H2020 projects ECOPOTENTIAL (grant number: 641762), e-shape (grant number: 820852), eLTER PLUS (grant number: 871128), by the Italian National Biodiversity Future Center (NBFC), National Recovery and Resilience Plan (NRRP; mission 4, component 2, investment 1.4 of the Ministry of University and Research, funded by the European Union–NextGenerationEU; project code CN00000033), and by the ITINERIS NRRP Italian infrastructure project (project code No. IR0000032 - ESFRI Environment)

Factors affecting the winter roost site selection of the Stone-curlew Burhinus oedicnemus (Charadriiformes, Burhinidae) in central Italy

A thorough knowledge of winter ecology is important for an effective conservation of bird species, and in order to understand how they would cope with ongoing changes due to global warming. This is particularly relevant for short-distance migrants, whose migration strategies could be rather flexible. In recent years these migrants have been observed to change their seasonal movements both in timing and distances. These changes could be closely related to their overwintering strategies. The Stone-curlew is a short-distance migrant whose winter ecology is poorly studied and almost no data are available regarding the way these birds select their roost sites during the non-breeding season. The aim of this study was to identify the environmental factors affecting Stone- curlews roost choice in an area of the Grosseto province at three different space-use scale of the species (local, sight-field and foraging scale). The broader spatial scale considered was derived from the length of foraging flights, between roost and foraging areas, identified by means of 19,295 records, from eight GPS tagged individuals. Stone- curlew surveys provided roost locations. Roost sites were used to develop three models for roost presence at the three spatial scales, using the Maximum Entropy algorithm (MaxEnt), a presence-only data approach. A bias file for modelling the sampling effort in the study area, by fitting a kernel density estimate (KDE) to the roost locations, was calculated and used as probability grid for background points sampling. Land cover, topography variables, farms proximity, hunting disturbance and road density were chosen as predictors. The model at the local scale was the only reliable model emerged and it indicated seven variables as the most important for Stone-curlew winter roost selection: slope, road density and the cover of five land cover categories. The accuracy of the models for the other two spatial scale was not different from a random prediction. Identifying features at different space-use scale which determine winter roosts presence could be essential to improve the species monitoring as well as to help the drawing of habitat management plans addressed to the species usually associated with farmland landscapes, which are among the most threatened species in Europe.Identifying features at different space-use scale which determine winter roosts presence could be essential to improve the species monitoring as well as to help the drawing of habitat management plans addressed to the species usually associated with farmland landscapes, which are among the most threatened species in Europe

Modelling Multi-Year Carbon Fluxes in the Arctic Critical Zone (Spitzbergen, NO)

<p>Presentation given at the Svalbard Science Conference 2023 (SSC23) that took place in Oslo, Norway on October 31st-November 01st, 2023. </p><p>Vegetation and soil regulate the terrestrial carbon cycle and contribute to the atmospheric CO2 concentration and Earth climate. The Arctic soil plays a major role in this cycle as the extension of permafrost areas is around 25% of the land in the Northern hemisphere and it is estimated that permafrost stores 2-3 times the atmospheric carbon. In the Holocene, the tundra has acted as a carbon sink, but it is not clear if the fast Arctic climate change will turn it into a carbon source. Yet, data regarding Arctic carbon fluxes are scarce and modelling of their fate is affected by large uncertainties. </p><p>With the aim of investigating the tundra carbon fluxes dynamics on the high Arctic, CNR established the Bayelva Critical Zone Observatory at the Ny Ålesund research station in Svalbard since 2019, equipped with an Eddy Covariance tower and portable flux chambers for the measurement of Gross Primary Productivity (GPP) and of Ecosystem Respiration (ER) variability at the point scale, making it possible to build empirical models that correlate such variables to climate and environmental parameters such as temperature, irradiance, moisture and phenology. A first model, published in 2022, identified temperature, solar irradiance, soil moisture and green fractional cover as drivers. Further measurements done in 2021 and 2022 adding further sites in the Bayelva basin, allowed us to enlarge the scale of application of the model. A further step will be the use of the high-resolution satellite data of the VENmS mission (4 meters, 1 day revisit time) to extend the modelling of GPP over the entire Broegger peninsula, facilitating the spatial upscaling of measured fluxes,identifying the main variables to be used in general vegetation models and allowing future projections of carbon fluxes under different climate change scenarios in the high Arctic tundra.</p&gt

Net Ecosystem Exchange, Ecosystem Respiration and meteoclimatic data of Alpine grasslands at Nivolet Plain, Gran Paradiso National Park, Italy 2017-2023

<p>This dataset presents georeferenced measurements collected at the Nivolet Plain in Gran Paradiso National Park (GPNP), western Italian Alps. The dataset includes the Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER) and meteo-climatic variables, i.e. air and soil temperature, air relative humidity, soil volumetric water content, atmospheric pressure and solar irradiance. The measurements were conducted between 2017 and 2023 at five different sites at an elevation of approximately 2550-2750 meters a.s.l.</p><p>To estimate NEE and ER, we employed the flux chamber method, measuring the temporal variation of carbon dioxide (CO2) concentration inside the chamber over a period of about 90 seconds. We used a customized portable non-steady-state dynamic flux chamber, paired with an InfraRed Gas Analyzer (IRGA) and a portable weather station. Measurements were taken at around 20 points per site during the snow-free season, spanning from June to October.</p><p>The dataset is provided in a comma-separated text file (.csv) format. Each record corresponds to a single measurement point, with semicolons used as separators. The "NA" notation indicates values that are not available or have been excluded during quality control processes (e.g., due to battery failure). We use point as decimal separator.</p><p>The sign convention for the fluxes is: a negative value indicates a CO2 flux from the atmosphere to the ecosystem, while a positive value represents a CO2 flux from the soil/ecosystem to the atmosphere. Consequently, ER values are positive, while NEE values can be positive or negative. The units for NEE and ER fluxes are molCO2 m-2 day-1 and μmolCO2 m-2 second-1. The first values in each record of the dataset indicate the observation details (sampling date, site, etc.), followed by the corresponding measured or calculated variables. NEE and ER values were estimated from the slope of the linear regression of CO2 concentration over time (ppm s-1) using a laboratory calibration curve.</p><p>The calibration curve was created by relating known and pre-set CO2 fluxes (within the range expected in the field) with the corresponding measured slopes. The flux values were then scaled up based on the area of the chamber base (0.036 m2) and adjusted using the ratio of atmospheric pressure and air temperature during the measurement to those recorded during the calibration in the laboratory.</p&gt