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FIGURE 1 in Description of the sexuales of Myzodium modestum (Hottes) (Hemiptera: Aphididae) discovered in the Swiss Alps
FIGURE 1. Ovipara and alate male of M. modestum: (A) habitus of ovipara; (B) abdomen of male; (C) siphunculus of ovipara; (D) hind tibia of ovipara with pseudosensoria; (E) fore wing of male. Scale bars = 300 µm, 120 µm, 50 µm, 50 µm, 120 µm, respectively.
Fig. 8 in A new vertebrate for Europe: the discovery of a range-restricted relict viper in the western Italian Alps
Fig. 8. Currently known extent of occurrence of Vipera walser sp. nov. (in blue) and V. berus (in red) in north western Italy
Fig. 3 in A new vertebrate for Europe: the discovery of a range-restricted relict viper in the western Italian Alps
Fig. 3. Results of the non-metric multidimensional scaling (nMDS, Bray – Curtis with similarity index), for the females (left) and males (right) conducted separately. The following variables were considered: subcaudals, crown scales, apicals, perioculars, parietals and loreals (only on the right side because V. walser show a much higher degree of asymmetry on loreal scales count, compared to V. berus). The analysis was carried out on V. walser and three groups of V. berus having the same number of samples, in order to evaluate intraspecific variability. V. walser are in red. The graphs show V. walser to be well differentiated in respect to the three groups of V. berus, which are mostly overlapping.
Fig. 10 in A new vertebrate for Europe: the discovery of a range-restricted relict viper in the western Italian Alps
Fig. 10. Current and projected (2035; CMIP 5 [IPPC Fifth Assessment]) mean annual rainfall and mean minimum temperatures (° C) for the months May – October within the current range of Vipera walser sp. nov.
Fig. 9 in A new vertebrate for Europe: the discovery of a range-restricted relict viper in the western Italian Alps
Fig. 9. Habitat of Vipera walser sp. nov. Valle Strona at about 1650 m (upper left), Valle Strona at about 1800 m (upper right), Valle Mastallone at 2070 m (bottom left) and Valle della Vecchia at 1830 m (bottom right)
Fig. 7 in A new vertebrate for Europe: the discovery of a range-restricted relict viper in the western Italian Alps
Fig. 7. Variation in head scalation in adult female (upper four photographs) and adult male (lower four photographs) of Vipera walser sp. nov.
Fig. 4 in A new vertebrate for Europe: the discovery of a range-restricted relict viper in the western Italian Alps
Fig. 4. Discriminant analysis, based on the six meristic variables, correctly classifies 94 % of females (left) and 88 % of males. The strongest discriminatory variables are crown scales and parietals. Blue = V. berus; red = V. walser
Gravity, Free-Air and Bouguer Anomaly Data in the Ivrea-Verbano Zone (Western Alps, Italy)
<p>Gravity dataset collected in the Ivrea-Verbano Zone (IVZ, Western Alps, Italy). </p><p>The data was collected in the frame of a gravity-based investigation and modelling of the Ivrea Geophysical Body. </p><p>For citation and further details on the work see Scarponi et al. (2020, GJI): <a href="https://doi.org/10.1093/gji/ggaa263">https://doi.org/10.1093/gji/ggaa263</a></p><p>The file contains the gravity data collected in the IVZ region, including free-air anomaly and Bouguer gravity anomaly (in mGal).</p><p>Longitude, Latitude coordinates are in degrees, elevation in meters.</p><p>Uncertainty on the final gravity data products and gravity data is 1 mGal.</p><p>---</p><p>Data collection, as well as the associated research, were supported by the Swiss National Science Foundation (SNF) (grant numbers PP00P2_157627 and PP00P2_187199).</p>
Land use and land cover scenarios for the Maurienne valley (French Alps) at 2085 horizon produced using CLUMPY model
<p>We built three contrasted future LULC scenarios from 2020 to 2085 with the CLUMPY model (Mazy and Longaretti, 2022). The CLUMPY model is an innovative model of land use and land cover change comprising a calibration-estimation module separate from a non-biased allocation module. It is calibrated by using time series of past LULC maps (Mazy and Longaretti, 2022). The model then calculates transition probabilities for each LULC class according to relevant spatial explanatory variables. Next, the model can produce maps of future LULC distributions according to information it learned during the calibration-estimation phase. This model has the benefits of being easy to use, proposing nonbiased allocation methods and producing scenarios of future LULC change either by adjusting manually the matrix of LULC transitions probabilities (used for the Conservation and Tourism scenarios) or by training the model on specific areas of the past time series (only used for the Conservation scenario).</p>
Focal mechanism solutions of 162 earthquakes occurred between 2014 and 2023 in the Southeastern Alps
<p>Focal mechanism solutions of 162 earthquakes occurred between 2014 and 2023 in the Southeastern Alps</p> <p>Andrea Magrin, Monica Sugan, Adriano Snidarcig, Maria Adelaide Romano, Mariangela Guidarelli, Marco Santulin, Paolo Di Bartolomeo, Angela Saraò</p> <p><strong> </strong></p> <p>Description</p> <p>This dataset includes focal mechanisms (FPFIT_solution.pdf, FPFIT_solution.csv) obtained with the FPFIT algorithm (Reasenberger and Oppenheimer, 1985) for 162 earthquakes with magnitudes Md between 2.8 and 3.6 that occurred between 2014 and 2023 in the Southeastern Alps region (45°N-47.5°N and 10°E-15°E). This dataset updates the catalogues Saraò et al., 2021a, b, and Sugan et al., 2020.</p> <p>The first polarities used to calculate the focal mechanisms are manually picked from seismograms recorded by the National Institute of Oceanography and Applied Geophysics (OGS) northeastern Italy seismic and deformation network (Sistema di Monitoraggio terrestre dell’Italia Nord Orientale - SMINO) (Priolo et al., 2005; Bragato et al., 2011; Bragato et al., 2021) and various Italian and European seismological observatories using temporary and permanent seismic networks. The permanent and temporary networks are as follows: CH – Swiss Seismological Service, 1983; GU – University of Genoa, 1967; IV – INGV Seismological Data Centre, 2005; MN – MedNet Project Partner Institutions, 1990; NI – OGS and University of Trieste, 2002; OE – ZAMG, 1987, 1990; OX – OGS, 2016; RF – University of Trieste, 1993; SI - Sudtirol Network, Italy (https://www.fdsn.org/networks/detail/S.I/; SL – Slovenian Environment Agency, 1990; ST – Geological Survey – Provincia Autonoma Di Trento, 1981; XT – Zhao et al, 2018; Y5 – Swiss Seismological Service, 2022; Z3 – AASN, 2015; ZO - Massa et al, 2021; ZS - Heit et al., 2017.</p> <p>The locations of the events are taken from the seismological bulletins of the OGS and the associated catalogues, which were published continuously from 1977 to 2014 (annual files) and are accessible via the Internet (http://www.crs.ogs.it/bollettino/RSFVG/). Since 2015, annual catalogues have been published in text format and annual bulletins in Quakeml format (Snidarcig et al., 2015, 2016, 2017, 2018, 2019, 2020a, 2021a, 2022a).</p> <p>In addition, unpublished solutions calculated for the studied area are extracted, reviewed and added from the annual reports of the OGS (Snidarcig et al., 2020b, 2021b, 2022b, 2023).</p> <p><strong> </strong></p> <p>References:</p> <p>Bragato, P.L., Di Bartolomeo, P., Pesaresi, D., Plasencia Linares, M., Saraò, A.: Acquiring, archiving, analyzing and exchanging seismic data in real time at the Seismological Research Center of the OGS in Italy, Ann. Geophys. 54, 67–75, https://doi.org/10.4401/ag-4958, 2011.</p> <p>Bragato, P. L., Comelli, P., Saraò, A., Zuliani, D., Moratto, L., Poggi, V., Rossi, G., Scaini, C., Sugan, M., Barnaba, C., Bernardi, P., Bertoni, M., Bressan, G., Compagno, A., Del Negro, E., Di Bartolomeo, P., Fabris, P., Garbin, M., Grossi, M., Magrin, A., Magrin, E., Pesaresi, D., Petrovic, B., Linares, M. P. P., Romanelli, M., Snidarcig, A., Tunini, L., Urban, S., Venturini, E., Parolai, S.: The OGS–Northeastern Italy Seismic and Deformation Network: Current Status and Outlook, Seismol. Res. Lett., 92, 1704–1716, https://doi.org/10.1785/0220200372, 2021. </p> <p>Priolo, E., Barnaba, C., Bernardi, P., Bernardis, G., Bragato, P.L., Bressan, G., Candido, M., Cazzador, E., Di Bartolomeo, P., Durì, G., Gentili, S., Govoni, A., Klinc, P., Kravanja, S., Laurenzano, G., Lovisa, L., Marotta, P., Michelini, A., Ponton F., Restivo, A., Romanelli, A., Snidarcig, A., Urban, S., Vuan, A., Zuliani, D.: Seismic monitoring in northeastern Italy: A ten-year experience, Seismol. Res. Lett., 76, 446–454, https://doi.org/10.1785/gssrl.76.4.446, 2005.</p> <p>Reasenberg, P., Oppenheimer, D.: FPFIT, FPPLOT and FPPAGE: Fortran computer programs for calculating and displaying earthquake fault-plane solutions, Open-File Rep., 85-739, USGS, Menlo Park, 109 pp., 1985.</p> <p>Saraò A., Sugan M., Bressan G., Renner G., Restivo A.: Focal mechanisms of Southeastern Alps and surroundings, doi: 10.5281/zenodo.4284971, 2020, a.</p> <p>Saraò, A., Sugan, M., Bressan, G., Renner, G., Restivo, A.: A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019, Earth Syst. Sci. Data, 13, 2245–2258, https://doi.org/10.5194/essd-13-2245-2021, 2021, b.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.6092/58ff169a-2f02-46ae-908a-bdfcacea069c, 2015.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.6092/a608d853-755e-4177-aada-992857ccb44e, 2016.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.6092/3ff3c323-d7a4-4183-bea0-1a53814ac8b9, 2017.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.6092/c53f37ce-bcf3-453c-a2cf-1894d48cfbbb, 2018.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.6092/58ff169a-2f02-46ae-908a-bdfcacea069c, 2019.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.13120/108b8d94-361a-45f3-8195-fc4e8f73d264, 2020a</p> <p>Snidarcig, A., Bragato, P.L., Barnaba, C., Bertoni, M., Bressan, G., Comelli, P., Del Negro, E., Fabris, P., Gentili, S., Moratto, L., Peresan, A., Peruzza, L., Poggi, V., Priolo, E ,Rebez, A., Rossi, G., Sandron, D., Saraò, A., Scaini, C., Sugan, M., Tufaro, T., Urban, S., Zuliani, D., Bernardi, P., Di Bartolomeo, P., Grossi, M., Pesaresi, D., Pettenati, F., Plasencia Linares, M., Ponton, C., Restivo, A., Romanelli, M.: Accordo tra la Regione Autonoma Friuli Venezia Giulia e l’OGS per la collaborazione nell’ambito del monitoraggio sismico e meteomarino Relazione 2019 – Ambito Sismologico. Relazione Interna OGS 2020/22 CRS 7 SIRE. http://hdl.handle.net/20.500.14083/6700, 2020b.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.13120/8b252b09-314f-456f-812a-b05268ecd001, 2021a.</p> <p>Snidarcig, A., Bragato, P.L., Barnaba, C., Bertoni, M., Bressan, G., Comelli, P., Del Negro, E., Gentili, S., Magrin, A., Moratto, L., Peresan, A., Rebez, A., Rossi, G., Sandron, D., Saraò, A., Sugan, M., Tamaro, A., Urban, S., Zuliani, D., Bernardi, P., Compagno, A., Di Bartolomeo, P., Bottaro, G., Grossi, M., Magrin, E., Pesaresi, D., Pettenati, F., Plasencia Linares, M., Poggi, V., Ponton, C., Restivo, A., Romanelli, M.: Accordo tra la Regione Autonoma Friuli Venezia Giulia e l’OGS per la collaborazione nell’ambito del monitoraggio sismico e meteomarino. Relazione 2020 – Ambito Sismologico. Relazione Interna OGS 2021/29 CRS 7 RETI. https://hdl.handle.net/20.500.14083/18770, 2021b.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, http://www.crs.ogs.it/bollettino_new/, last access 16/02/2024, 2022a.</p> <p>Snidarcig, A., Bragato, P.L., Barnaba, C., Bertoni, M., Bressan, G., Comelli, P., Del Negro, E., Gentili, S., Klin, P., Laurenzano, G., Magrin, A., Moratto, L., Parolai, S., Peresan, A., Petrovic, B., Poggi, V., Priolo, E., Rebez, A., Rossi, G., Sandron, D., Saraò, A., Scaini, C., Sugan, M., Tamaro, A., Urban, S., Vuan, A., Zuliani, D., Bernardi, P., Compagno, A., Di Bartolomeo, P., Bottaro, G., Grossi, M., Magrin, E., Pesaresi, D., Pettenati, F., Plasencia Linares, M., Ponton, C., Romanelli, M.: Accordo tra la Regione Autonoma Friuli Venezia Giulia e l’OGS per la collaborazione nell’ambito del monitoraggio sismico e meteomarino. Relazione 2021 – Ambito Sismologico. Relazione 2022/74 Sez. CRS 7 RETI. https://hdl.handle.net/20.500.14083/17863, 2022b.</p> <p>Snidarcig, A., Bragato, P.L., Barnaba, C., Bertoni, M., Brondi, P., Comelli, P., Del Negro, E., Gentili, S., Magrin, A., Magrin, E., Moratto, L., Peresan, A., Petrovic, B., Poggi, V., Rebez, A., Rossi, G., Sandron, D., Saraò, A., Scaini, C., Sugan, M., Tunini, L., Zuliani, D., Bernardi, P., Compagno, A., Di Bartolomeo, P., Bottaro, G., Grossi, M., Pesaresi, D., Pettenati, F., Plasencia Linares, M., Ponton, C., Romanelli, M.: Accordo tra la Regione Autonoma Friuli Venezia Giulia e l’OGS per la collaborazione nell’ambito del monitoraggio sismico e meteomarino Relazione 2022 – Ambito Sismologico. Relazione Interna OGS 2023/72. https://hdl.handle.net/20.500.14083/24083, 2023.</p> <p>Sugan, M., Renner, G., Bressan, G., Restivo, A., Saraò, A.: First motion data and focal mechanism solutions of 108 earthquakes occurred between 1928 and 2019 in the Southeastern Alps [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4284929, 2020</p> <p>CH - Swiss Seismological Service (SED) At ETH Zurich: National Seismic Networks of Switzerland [Data set], ETH Zürich, Zurich, <a href="https://doi.org/10.12686/sed/networks/ch">https://doi.org/10.12686/sed/networks/ch</a>, 1983.</p> <p>GU - University of Genoa: Regional Seismic Network of North Western Italy [Data set], International Federation of Digital Seismograph Networks, <a href="https://doi.org/10.7914/SN/GU">https://doi.org/10.7914/SN/GU</a>, 1967.</p> <p>IV - INGV Seismological Data Centre: Rete Sismica Nazionale (RSN) [Data set], Istituto Nazionale di Geofisica e Vulcanologia (INGV), Italy, <a href="https://doi.org/10.13127/SD/X0FXnH7QfY">https://doi.org/10.13127/SD/X0FXnH7QfY</a>, 2005.</p> <p>MN - MedNet Project Partner Institutions: Mediterranean Very Broadband Seismographic Network (MedNet) [Data set], Istituto Nazionale di Geofisica e Vulcanologia (INGV), <a href="https://doi.org/10.13127/SD/fBBBtDtd6q">https://doi.org/10.13127/SD/fBBBtDtd6q</a>, 1990.</p> <p>NI - OGS – Istituto Nazionale Di Oceanografia E Di Geofisica Sperimentale – and University Of Trieste: North-East Italy Broadband Network [Data set], International Federation of Digital Seismograph Networks, <a href="https://doi.org/10.7914/SN/NI">https://doi.org/10.7914/SN/NI</a>, 2002.</p> <p>OE - ZAMG – Zentralanstalt Für Meterologie Und Geodynamik: Austrian Seismic Network [Data set], International Federation of Digital Seismograph Networks, <a href="https://doi.org/10.7914/SN/OE">https://doi.org/10.7914/SN/OE</a>, 1987.</p> <p>OX - OGS – Istituto Nazionale Di Oceanografia E Di Geofisica Sperimentale: North-East Italy Seismic Network [Data set], International Federation of Digital Seismograph Networks, <a href="https://doi.org/10.7914/SN/OX">https://doi.org/10.7914/SN/OX</a>, 2016.</p> <p>RF - University of Trieste: Friuli Venezia Giulia Accelerometric Network [Data set], International Federation of Digital Seismograph Networks, Trieste, <a href="https://doi.org/10.7914/SN/RF">https://doi.org/10.7914/SN/RF</a>, 1993.</p> <p>SL - Slovenian Environment Agency, Seismic Network of the Republic of Slovenia [Data set]. International Federation of Digital Seismograph Networks. <a href="https://doi.org/10.7914/SN/SL">https://doi.org/10.7914/SN/SL</a>, 1990</p> <p>ST - Geological Survey – Provincia Autonoma Di Trento: Trentino Seismic Network, International Federation of Digital Seismograph Networks [Data set], Trento, <a href="https://doi.org/10.7914/SN/ST">https://doi.org/10.7914/SN/ST</a>, 1981.</p> <p>XT - Zhao, L., Paul, A., Solarino, S., & RESIF., Seismic network XT: CIFALPS-2 temporary experiment (China-Italy-France Alps seismic transect #2 [Data set]. RESIF - Réseau Sismologique et géodésique Français. <a href="https://doi.org/10.15778/RESIF.XT2018">https://doi.org/10.15778/RESIF.XT2018</a>, 2018</p> <p>Y5 - Swiss Seismological Service (SED) at ETH Zurich, Swiss Contribution to AdriaArray Temporary Network; ETH Zurich. Other/Seismic network. <a href="https://doi.org/10.12686/SED/NETWORKS/Y5">https://doi.org/10.12686/SED/NETWORKS/Y5</a>, 2022</p> <p>Z3 - AlpArray Seismic Network: AlpArray Seismic Network (AASN) temporary component [Data set], AlpArray Working Group, <a href="https://doi.org/10.12686/alparray/z3_2015">https://doi.org/10.12686/alparray/z3_2015</a>, 2015.</p> <p>ZO - Massa, M., Rizzo, A. L., Lorenzetti, A., Lovati, S., D’Alema, E., Puglia, R., … Luzi, L.: Rete di monitoraggio multiparametrico del Garda (Nord Italia) - PDnet (Version 1.0) [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). <a href="https://doi.org/10.13127/SD/YHCFOMCBO_">https://doi.org/10.13127/SD/YHCFOMCBO_</a>, 2021.</p> <p>ZS - Heit, B.; Weber, M.; Tilmann, F.; Haberland, C.; Jia, Y.; Pesaresi, D.: The Swath-D Seismic Network in Italy and Austria. GFZ Data Services. Other/Seismic Network. <a href="http://doi.org/10.14470/MF7562601148">doi:10.14470/MF7562601148</a>, 2017.</p>
Biomass production at 2085 horizon for the Maurienne valley (French Alps) estimated using a Bayesian Belief Network
<p>In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: while an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of regional grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified.</p> <p>We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2100. We built a Bayesian Belief Network (BBN) from long-term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution, and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios.</p> <p>Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the Conservation LULC scenario (-28% on average between 2020 and 2085), followed by the Tourism development scenario (-7%) and the Business-as-Usual scenario (+3%). Climate change under RCP 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth. Intensification somewhat compensated effects of climate and LULC changes on biomass production, whereas silvopastoralism offered only marginal gains. The Bayesian network model explicitly captured a future increase in interannual variability in biomass production.</p> <p>Synthesis and application: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannnual variability threaten the current grass-based Protected Designation of Origin production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions. </p>
Vulnerability tools - Austrian Alps (Austria)
<p>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Austrian Alps Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</p>
Vulnerability tools - Swiss Alps (Switzerland)
<p>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Swiss Alps Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</p>
Vulnerability tools - Eastern Alps (Italy)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Eastern Alps Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
Fig. 2 in Salix ×marchettii (Salicaceae), a new nothospecies from the Apuan Alps (Northern Tuscany, Central Italy)
Fig. 2. – Salix ×marchettii M. Merli & F. Mart. A. Mixed indumentum on young twig: silky and matted hairs. B. Connate filaments.
Fig. 3. – Poils d in Androsace albimontana (Primulaceae): une nouvelle espèce des Alpes (France, Suisse, Italie) à distinguer de A. pubescens
Fig. 3. – Poils d'Androsace L. A. A. albimontana D. Jord. & Jacquemoud; B. A. alpina (L.) Lam.; C. A. pubescens DC.
Fig. 2 in Androsace albimontana (Primulaceae): une nouvelle espèce des Alpes (France, Suisse, Italie) à distinguer de A. pubescens
Fig. 2. – Carte topographique de situation de la zone d'étude, avec mention des localités citées dans le texte. La localité type est signalée par une étoile. [source Wikimedia Commons (Wikisoft*) sous licence GNU Free Documentation License ver. 1.2]
Fig. 1 in Androsace albimontana (Primulaceae): une nouvelle espèce des Alpes (France, Suisse, Italie) à distinguer de A. pubescens
Fig. 1. – Carte géologique synoptique de la zone d'étude avec les massifs cristallins et les hautes chaînes calcaires des nappes helvétiques. [Carte modifiée d'après GIDON (2020)]
Fig. 8 in Analyse de la flore des Alpes. 3: biologie et phénologie
Fig. 8. – Pourcentages de taxons potentiellement en fleurs chaque mois de l'année, calculés sur les nombres totaux de taxons indiqués entre crochets pour les cinq catégories de longévité
Fig. 7 in Analyse de la flore des Alpes. 3: biologie et phénologie
Fig. 7. – Carte de la dition avec, par division administrative, le pourcentage de taxons potentiellement en fleurs en juillet, seule la flore indigène totale (endémiques+indigènes) étant ici considérée. Quatre classes sont définies: blanc: ≤ 80,5%; gris clair: 80,6-82,5%; gris foncé: 82,6-84,5%; noir: ≥ 84,6%.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.