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Research data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Bukoski, Jacob; Cook-Patton, Susan C.; Melikov, Cyril; Ban, Hongyi; Chen, Jessica Liu; Goldman, Elizabeth D.; Harris, Nancy L.; Potts, Matthew D.;This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). When using these data, please cite: Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. Nature Communications 13(4206). doi: 10.1038/s41467-022-31380-7 The code for all analyses in Bukoski et al., 2022 (paper associated with this dataset) is available at https://github.com/jbukoski/GPFC (doi: 10.5281/zenodo.6588710).
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:Zenodo Funded by:EC | EdgeStressEC| EdgeStressThyrring, Jakob; Wegeberg, Susse; Blicher, Martin E.; Krause-Jensen, Dorte; Høgslund, Signe; Olesen, Birgit; Wiktor Jr, Jozef; Mouritsen, Kim N.; Peck, Lloyd S.; Sejr, Mikael K.;The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.3920534&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Embargo end date: 31 Dec 2021Publisher:Zenodo Funded by:EC | GEMexEC| GEMexLelli, Matteo; Cabassi, Jacopo; Nisi, Barbara; Vaselli, Orlando; Tassi, Franco;The dataset CO2_flux_measurements_Acoculco contains data on CO2 fluxes, coordinates (UTM), air temperature, atmospheric pressure measured in selected sites belonging to the Acoculco Geothermal Field: in particular, the areas named Lagunilla, Alcaparrosa, Los Azufres and also the area between them were investigated. CO2 flux measurements were performed using the accumulation chamber method. The dataset Field_meas_Acoculco_waters reports the ID, coordinates (UTM), Altitude (m.a.s.l.), temperature, flow rate, pH, Electrical Conductivity and Dissolved Oxygen for water samples collected in the central sector of the Acoculco geothermal field, but also in other sectors located inside and outside the Acoculco caldera. Total depth is also included for samples collected from water wells. The dataset Chemical_isotopic_data_Acoculco_waters reports major and minor chemical components and stable isotopic composition for hydrogen and oxygen determined in collected water samples in Acoculco geothermal field. Calculated partial pressures (in bars and log10-value) and CO2 concentrations of dissolved CO2 were also included. The dataset Chemical_isotopic_data_Acoculco_gas reports chemical and isotopic data for collected samples from Los Azufres and Alcaparrosa natural gas manifestations.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.3727572&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020 United StatesPublisher:U.S. Geological Survey Croke, Mary R; Hackley, Paul C; Jubb, Aaron M; Burruss, Robert C; Beaven, Amy E;doi: 10.5066/p9gdb7f0
Fluorescence spectroscopy via confocal laser scanning microscopy (CLSM) was used to analyze ancient sedimentary organic matter, including Tasmanites microfossils in Devonian shale and Gloecapsomorpha prisca (G. prisca) in Ordovician kukersite from North American basins. We examined fluorescence emission as a function of excitation laser wavelength, sample orientation, and with respect to location within individual organic entities and along organic matter chemical transects. Results from spectral scans of the same field of view in Tasmanites with different laser lines showed progressive red-shift in emission maxima with longer excitation wavelengths. This result indicates steady-state Tasmanites fluorescence emission is an overlapping combination of emission from multiple distinct fluorophore functions. Stokes shift decreased with increasing excitation wavelength, further suggesting the presence of multiple fluorophore functions with different S1 -> S0 transition energies. This observation also indicates that at longer excitation wavelengths, less absorbed light energy is dissipated via collisional transfer than at shorter excitation wavelengths and may suggest fewer polar functions are preferentially absorbing. Confirming earlier results, emission spectra observed from high fluorescence intensity regions (fold apices) in individual Tasmanites are blue-shifted relative to emission from other locations in the same microfossil. We suggest high intensity emission is from photoselective alignment of polarized excitation with the fluorophore absorption and emission transition moment. The blue shift observed in regions of high intensity emission may be due to relative absence of polar species, e.g., bridging ether or ester functions, although this could not be confirmed with preliminary time-of-flight secondary ion mass spectrometry (TOF-SIMS) analysis. Tasmanites occurring in consolidated sediments are flattened from original spherical morphology and, in optical microscopy, this burial deformation results in generally parallel extinction (strain-influenced) and positive elongation. The deformation also induces fluorescence anisotropy observed as variations in emission wavelength when samples are measured parallel to bedding, whereas this effect is absent in bedding-normal view. Evaluation of fluorescence emission on compositional transects from G. prisca-rich source layers into adjacent reservoir layers indicates decrease in fluorescence intensity and spectral red-shift (increase in full-width half-maximum with increasing red portion of the half-width). These results may suggest an increase in fluorescence quenching across the source-to-reservoir transition zone, consistent with an increase in aromaticity following petroleum expulsion and migration. These observations are supported by increasing reflectance values measured across similar micro-scale transects. Our results highlight the applicability of CLSM as a broad and under-utilized approach for the characterization of sedimentary organic matter and are discussed with perspective toward petroleum processes and thermal indices research.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:Smithsonian Tropical Research Institute Authors: Paton, Steven;doi: 10.25573/data.10059455.v28 , 10.25573/data.10059455.v6 , 10.25573/data.10059455.v30 , 10.25573/data.10059455.v34 , 10.25573/data.10059455.v45 , 10.25573/data.10059455.v33 , 10.25573/data.10059455 , 10.25573/data.10059455.v13 , 10.25573/data.10059455.v9 , 10.25573/data.10059455.v31 , 10.25573/data.10059455.v22 , 10.25573/data.10059455.v14 , 10.25573/data.10059455.v27 , 10.25573/data.10059455.v11 , 10.25573/data.10059455.v44 , 10.25573/data.10059455.v15 , 10.25573/data.10059455.v38 , 10.25573/data.10059455.v17 , 10.25573/data.10059455.v16 , 10.25573/data.10059455.v2 , 10.25573/data.10059455.v29 , 10.25573/data.10059455.v12 , 10.25573/data.10059455.v32 , 10.25573/data.10059455.v39 , 10.25573/data.10059455.v26 , 10.25573/data.10059455.v19 , 10.25573/data.10059455.v41 , 10.25573/data.10059455.v25 , 10.25573/data.10059455.v23 , 10.25573/data.10059455.v10 , 10.25573/data.10059455.v20 , 10.25573/data.10059455.v21 , 10.25573/data.10059455.v24 , 10.25573/data.10059455.v1 , 10.25573/data.10059455.v8 , 10.25573/data.10059455.v3 , 10.25573/data.10059455.v5 , 10.25573/data.10059455.v46 , 10.25573/data.10059455.v4 , 10.25573/data.10059455.v42 , 10.25573/data.10059455.v18 , 10.25573/data.10059455.v43 , 10.25573/data.10059455.v40 , 10.25573/data.10059455.v7
doi: 10.25573/data.10059455.v28 , 10.25573/data.10059455.v6 , 10.25573/data.10059455.v30 , 10.25573/data.10059455.v34 , 10.25573/data.10059455.v45 , 10.25573/data.10059455.v33 , 10.25573/data.10059455 , 10.25573/data.10059455.v13 , 10.25573/data.10059455.v9 , 10.25573/data.10059455.v31 , 10.25573/data.10059455.v22 , 10.25573/data.10059455.v14 , 10.25573/data.10059455.v27 , 10.25573/data.10059455.v11 , 10.25573/data.10059455.v44 , 10.25573/data.10059455.v15 , 10.25573/data.10059455.v38 , 10.25573/data.10059455.v17 , 10.25573/data.10059455.v16 , 10.25573/data.10059455.v2 , 10.25573/data.10059455.v29 , 10.25573/data.10059455.v12 , 10.25573/data.10059455.v32 , 10.25573/data.10059455.v39 , 10.25573/data.10059455.v26 , 10.25573/data.10059455.v19 , 10.25573/data.10059455.v41 , 10.25573/data.10059455.v25 , 10.25573/data.10059455.v23 , 10.25573/data.10059455.v10 , 10.25573/data.10059455.v20 , 10.25573/data.10059455.v21 , 10.25573/data.10059455.v24 , 10.25573/data.10059455.v1 , 10.25573/data.10059455.v8 , 10.25573/data.10059455.v3 , 10.25573/data.10059455.v5 , 10.25573/data.10059455.v46 , 10.25573/data.10059455.v4 , 10.25573/data.10059455.v42 , 10.25573/data.10059455.v18 , 10.25573/data.10059455.v43 , 10.25573/data.10059455.v40 , 10.25573/data.10059455.v7
Monthly and daily summary from Barro Colorado Island (BCI). Data organized in horizontal format for seasonal and inter-year comparisonsLocation 9°9'42.36"N, 79°50'15.67"WParameters: air temperature, relative humidity, wind speed and direction, precipitation, sea surface temperature, solar radiation (pyranometer), air pressure, soil moisture, runoff, potential evapotranspiration, wet/dry season starting datesLutz catchment is a 9.73ha protected watershed on BCIThe Lutz tower was built in 1972 and was originally 42m. In 2002 it was increased to 48mThe data from 48m should be considered a separate data series from the data at 42m. Wind speed is significantly higher at 48m due to the distance to the top of the canopy.The Clearing is a small, open area surrounded by forest and some buildings. Station established in 1972. Consists of a Stevenson screen with max/min thermometers and air pressure sensor. Temperature/humidity sensor, rain gauge and evaporation sensors are located at various locations around the screen.
https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
more_vert https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018 United StatesPublisher:U.S. Geological Survey Authors: Debra Higley-Feldman;doi: 10.5066/p9blvvq2
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2017Publisher:NERC Environmental Information Data Centre Reinsch, S.; Koller, E.; Sowerby, A.; De Dato, G.; Estiarte, M.; Guidolotti, G.; Kovács-Láng, E.; Kröel-Dula, G; Lellei-Kovács, E.; Larsen, K.S.; Liberati, D.; Ogaya, R; Peñuelas, J.; Ransijn, J.; Robinson, D.A.; Schmidt, I.K.; Smith, A.R.; Tietema, A.; Dukes, J.S.; Beier, C.; Emmett, B.A.;The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 06 Jan 2022Publisher:Dryad Jarvie, Scott; Ingram, Travis; Chapple, David; Hitchmough, Rodney; Nielsen, Stuart; Monks, Joanne M.;Although GPS coordinates for current populations are not included due to the potential threat of poaching, the climate variables for each species are provided. The records for extant gecko and skinks mainly came from the New Zealand's Department of Conervation Herpetofauna Database. After updating the taxonomy and cleaning the data to reflect the taxonomy as at 2019 of 43 geckos speceis recognised across seven genera and 61 species in genus, we then thinned the occurrence records at a 1 km resolution for all species then predicted distributions for those with > 15 records using species distribution models. The climate variables for each species were selected among annual mean temperature (bio1), maximum temperature of the warmest month (bio5), minimum temperature of the coldest month (bio6), mean temperature of driest quarter (bio9), mean temperature of wettest quarter (bio10), and precipitation of the driest quarter (bio17). To reduce multicollinearity in species distribution models for each species, we only retained climate variables with a variable inflation factor < 10. The climate variables were from the CHELSA database (https://chelsa-climate.org/), which can be freely downloaded for current and future scenarios. We also provide MCC tree files for the geckos and skinks. The phylogenetic trees have been constructed for NZ geckos by (Nielsen et al., 2011) and for NZ skinks by (Chapple et al., 2009). For geckos we used a subset of the sequences used by Nielsen et al. (2011) for four genes, two nuclear (RAG 1, PDC) and two mitochondrial (16S, ND2 along with flanking tRNA sequences). For skinks, we used sequences from Chapple et al. (2009) for one nuclear (RAG 1) and five mitochondrial (ND2, ND4, Cyt b, 12S and 16S) genes, and additional ND2 sequences for taxa not included in the original phylogeny (Chapple et al., 2011, p. 201). In total we used sequences for all recognised extant taxa (Hitchmough et al., 2016) as at 2019 except for three species of skink (O. aff. inconspicuum “Okuru”, O. robinsoni, and O. aff. inconspicuum “North Otago”) and two species of gecko (M. “Cupola” and W. “Kaikouras”) for which genetic data were not available. Aim: The primary drivers of species and population extirpations have been habitat loss, overexploitation, and invasive species, but human-mediated climate change is expected to be a major driver in future. To minimise biodiversity loss, conservation managers should identify species vulnerable to climate change and prioritise their protection. Here, we estimate climatic suitability for two speciose taxonomic groups, then use phylogenetic analyses to assess vulnerability to climate change. Location: Aotearoa New Zealand (NZ) Taxa: NZ lizards: diplodactylid geckos and eugongylinae skinks Methods: We built correlative species distribution models (SDMs) for NZ geckos and skinks to estimate climatic suitability under current climate and 2070 future-climate scenarios. We then used Bayesian phylogenetic mixed models (BPMMs) to assess vulnerability for both groups with predictor variables for life history traits (body size and activity phase) and current distribution (elevation and latitude). We explored two scenarios: an unlimited dispersal scenario, where projections track climate, and a no-dispersal scenario, where projections are restricted to areas currently identified as suitable. Results: SDMs projected vulnerability to climate change for most modelled lizards. For species’ ranges projected to decline in climatically suitable areas, average decreases were between 42–45% for geckos and 33–91% for skinks, although area did increase or remain stable for a minority of species. For the no-dispersal scenario, the average decrease for geckos was 37–52% and for skinks was 33–52%. Our BPMMs showed phylogenetic signal in climate change vulnerability for both groups, with elevation increasing vulnerability for geckos, and body size reducing vulnerability for skinks. Main conclusions: NZ lizards showed variable vulnerability to climate change, with most species’ ranges predicted to decrease. For species whose suitable climatic space is projected to disappear from within their current range, managed relocation could be considered to establish populations in regions that will be suitable under future climates.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | PARIS REINFORCEEC| PARIS REINFORCEDoukas, Haris; Spiliotis, Evangelos; Jafari, Mohsen A.; Giarola, Sara; Nikas, Alexandros;This dataset contains the underlying data for the following publication: Doukas, H., Spiliotis, E., Jafari, M. A., Giarola, S. & Nikas, A. (2021). Low-cost emissions cuts in container shipping: Thinking inside the box. Transportation Research Part D: Transport and Environment, 94, 102815, https://doi.org/10.1016/j.trd.2021.102815.
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visibility 24visibility views 24 download downloads 1 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:EC | REINFORCEEC| REINFORCEAuthors: Mina, Marco;Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.
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Research data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Bukoski, Jacob; Cook-Patton, Susan C.; Melikov, Cyril; Ban, Hongyi; Chen, Jessica Liu; Goldman, Elizabeth D.; Harris, Nancy L.; Potts, Matthew D.;This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). When using these data, please cite: Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. Nature Communications 13(4206). doi: 10.1038/s41467-022-31380-7 The code for all analyses in Bukoski et al., 2022 (paper associated with this dataset) is available at https://github.com/jbukoski/GPFC (doi: 10.5281/zenodo.6588710).
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:Zenodo Funded by:EC | EdgeStressEC| EdgeStressThyrring, Jakob; Wegeberg, Susse; Blicher, Martin E.; Krause-Jensen, Dorte; Høgslund, Signe; Olesen, Birgit; Wiktor Jr, Jozef; Mouritsen, Kim N.; Peck, Lloyd S.; Sejr, Mikael K.;The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.3920534&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Embargo end date: 31 Dec 2021Publisher:Zenodo Funded by:EC | GEMexEC| GEMexLelli, Matteo; Cabassi, Jacopo; Nisi, Barbara; Vaselli, Orlando; Tassi, Franco;The dataset CO2_flux_measurements_Acoculco contains data on CO2 fluxes, coordinates (UTM), air temperature, atmospheric pressure measured in selected sites belonging to the Acoculco Geothermal Field: in particular, the areas named Lagunilla, Alcaparrosa, Los Azufres and also the area between them were investigated. CO2 flux measurements were performed using the accumulation chamber method. The dataset Field_meas_Acoculco_waters reports the ID, coordinates (UTM), Altitude (m.a.s.l.), temperature, flow rate, pH, Electrical Conductivity and Dissolved Oxygen for water samples collected in the central sector of the Acoculco geothermal field, but also in other sectors located inside and outside the Acoculco caldera. Total depth is also included for samples collected from water wells. The dataset Chemical_isotopic_data_Acoculco_waters reports major and minor chemical components and stable isotopic composition for hydrogen and oxygen determined in collected water samples in Acoculco geothermal field. Calculated partial pressures (in bars and log10-value) and CO2 concentrations of dissolved CO2 were also included. The dataset Chemical_isotopic_data_Acoculco_gas reports chemical and isotopic data for collected samples from Los Azufres and Alcaparrosa natural gas manifestations.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020 United StatesPublisher:U.S. Geological Survey Croke, Mary R; Hackley, Paul C; Jubb, Aaron M; Burruss, Robert C; Beaven, Amy E;doi: 10.5066/p9gdb7f0
Fluorescence spectroscopy via confocal laser scanning microscopy (CLSM) was used to analyze ancient sedimentary organic matter, including Tasmanites microfossils in Devonian shale and Gloecapsomorpha prisca (G. prisca) in Ordovician kukersite from North American basins. We examined fluorescence emission as a function of excitation laser wavelength, sample orientation, and with respect to location within individual organic entities and along organic matter chemical transects. Results from spectral scans of the same field of view in Tasmanites with different laser lines showed progressive red-shift in emission maxima with longer excitation wavelengths. This result indicates steady-state Tasmanites fluorescence emission is an overlapping combination of emission from multiple distinct fluorophore functions. Stokes shift decreased with increasing excitation wavelength, further suggesting the presence of multiple fluorophore functions with different S1 -> S0 transition energies. This observation also indicates that at longer excitation wavelengths, less absorbed light energy is dissipated via collisional transfer than at shorter excitation wavelengths and may suggest fewer polar functions are preferentially absorbing. Confirming earlier results, emission spectra observed from high fluorescence intensity regions (fold apices) in individual Tasmanites are blue-shifted relative to emission from other locations in the same microfossil. We suggest high intensity emission is from photoselective alignment of polarized excitation with the fluorophore absorption and emission transition moment. The blue shift observed in regions of high intensity emission may be due to relative absence of polar species, e.g., bridging ether or ester functions, although this could not be confirmed with preliminary time-of-flight secondary ion mass spectrometry (TOF-SIMS) analysis. Tasmanites occurring in consolidated sediments are flattened from original spherical morphology and, in optical microscopy, this burial deformation results in generally parallel extinction (strain-influenced) and positive elongation. The deformation also induces fluorescence anisotropy observed as variations in emission wavelength when samples are measured parallel to bedding, whereas this effect is absent in bedding-normal view. Evaluation of fluorescence emission on compositional transects from G. prisca-rich source layers into adjacent reservoir layers indicates decrease in fluorescence intensity and spectral red-shift (increase in full-width half-maximum with increasing red portion of the half-width). These results may suggest an increase in fluorescence quenching across the source-to-reservoir transition zone, consistent with an increase in aromaticity following petroleum expulsion and migration. These observations are supported by increasing reflectance values measured across similar micro-scale transects. Our results highlight the applicability of CLSM as a broad and under-utilized approach for the characterization of sedimentary organic matter and are discussed with perspective toward petroleum processes and thermal indices research.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:Smithsonian Tropical Research Institute Authors: Paton, Steven;doi: 10.25573/data.10059455.v28 , 10.25573/data.10059455.v6 , 10.25573/data.10059455.v30 , 10.25573/data.10059455.v34 , 10.25573/data.10059455.v45 , 10.25573/data.10059455.v33 , 10.25573/data.10059455 , 10.25573/data.10059455.v13 , 10.25573/data.10059455.v9 , 10.25573/data.10059455.v31 , 10.25573/data.10059455.v22 , 10.25573/data.10059455.v14 , 10.25573/data.10059455.v27 , 10.25573/data.10059455.v11 , 10.25573/data.10059455.v44 , 10.25573/data.10059455.v15 , 10.25573/data.10059455.v38 , 10.25573/data.10059455.v17 , 10.25573/data.10059455.v16 , 10.25573/data.10059455.v2 , 10.25573/data.10059455.v29 , 10.25573/data.10059455.v12 , 10.25573/data.10059455.v32 , 10.25573/data.10059455.v39 , 10.25573/data.10059455.v26 , 10.25573/data.10059455.v19 , 10.25573/data.10059455.v41 , 10.25573/data.10059455.v25 , 10.25573/data.10059455.v23 , 10.25573/data.10059455.v10 , 10.25573/data.10059455.v20 , 10.25573/data.10059455.v21 , 10.25573/data.10059455.v24 , 10.25573/data.10059455.v1 , 10.25573/data.10059455.v8 , 10.25573/data.10059455.v3 , 10.25573/data.10059455.v5 , 10.25573/data.10059455.v46 , 10.25573/data.10059455.v4 , 10.25573/data.10059455.v42 , 10.25573/data.10059455.v18 , 10.25573/data.10059455.v43 , 10.25573/data.10059455.v40 , 10.25573/data.10059455.v7
doi: 10.25573/data.10059455.v28 , 10.25573/data.10059455.v6 , 10.25573/data.10059455.v30 , 10.25573/data.10059455.v34 , 10.25573/data.10059455.v45 , 10.25573/data.10059455.v33 , 10.25573/data.10059455 , 10.25573/data.10059455.v13 , 10.25573/data.10059455.v9 , 10.25573/data.10059455.v31 , 10.25573/data.10059455.v22 , 10.25573/data.10059455.v14 , 10.25573/data.10059455.v27 , 10.25573/data.10059455.v11 , 10.25573/data.10059455.v44 , 10.25573/data.10059455.v15 , 10.25573/data.10059455.v38 , 10.25573/data.10059455.v17 , 10.25573/data.10059455.v16 , 10.25573/data.10059455.v2 , 10.25573/data.10059455.v29 , 10.25573/data.10059455.v12 , 10.25573/data.10059455.v32 , 10.25573/data.10059455.v39 , 10.25573/data.10059455.v26 , 10.25573/data.10059455.v19 , 10.25573/data.10059455.v41 , 10.25573/data.10059455.v25 , 10.25573/data.10059455.v23 , 10.25573/data.10059455.v10 , 10.25573/data.10059455.v20 , 10.25573/data.10059455.v21 , 10.25573/data.10059455.v24 , 10.25573/data.10059455.v1 , 10.25573/data.10059455.v8 , 10.25573/data.10059455.v3 , 10.25573/data.10059455.v5 , 10.25573/data.10059455.v46 , 10.25573/data.10059455.v4 , 10.25573/data.10059455.v42 , 10.25573/data.10059455.v18 , 10.25573/data.10059455.v43 , 10.25573/data.10059455.v40 , 10.25573/data.10059455.v7
Monthly and daily summary from Barro Colorado Island (BCI). Data organized in horizontal format for seasonal and inter-year comparisonsLocation 9°9'42.36"N, 79°50'15.67"WParameters: air temperature, relative humidity, wind speed and direction, precipitation, sea surface temperature, solar radiation (pyranometer), air pressure, soil moisture, runoff, potential evapotranspiration, wet/dry season starting datesLutz catchment is a 9.73ha protected watershed on BCIThe Lutz tower was built in 1972 and was originally 42m. In 2002 it was increased to 48mThe data from 48m should be considered a separate data series from the data at 42m. Wind speed is significantly higher at 48m due to the distance to the top of the canopy.The Clearing is a small, open area surrounded by forest and some buildings. Station established in 1972. Consists of a Stevenson screen with max/min thermometers and air pressure sensor. Temperature/humidity sensor, rain gauge and evaporation sensors are located at various locations around the screen.
https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert https://dx.doi.org/1... arrow_drop_down Smithsonian figshareDataset . 2019License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018 United StatesPublisher:U.S. Geological Survey Authors: Debra Higley-Feldman;doi: 10.5066/p9blvvq2
The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2017Publisher:NERC Environmental Information Data Centre Reinsch, S.; Koller, E.; Sowerby, A.; De Dato, G.; Estiarte, M.; Guidolotti, G.; Kovács-Láng, E.; Kröel-Dula, G; Lellei-Kovács, E.; Larsen, K.S.; Liberati, D.; Ogaya, R; Peñuelas, J.; Ransijn, J.; Robinson, D.A.; Schmidt, I.K.; Smith, A.R.; Tietema, A.; Dukes, J.S.; Beier, C.; Emmett, B.A.;The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 06 Jan 2022Publisher:Dryad Jarvie, Scott; Ingram, Travis; Chapple, David; Hitchmough, Rodney; Nielsen, Stuart; Monks, Joanne M.;Although GPS coordinates for current populations are not included due to the potential threat of poaching, the climate variables for each species are provided. The records for extant gecko and skinks mainly came from the New Zealand's Department of Conervation Herpetofauna Database. After updating the taxonomy and cleaning the data to reflect the taxonomy as at 2019 of 43 geckos speceis recognised across seven genera and 61 species in genus, we then thinned the occurrence records at a 1 km resolution for all species then predicted distributions for those with > 15 records using species distribution models. The climate variables for each species were selected among annual mean temperature (bio1), maximum temperature of the warmest month (bio5), minimum temperature of the coldest month (bio6), mean temperature of driest quarter (bio9), mean temperature of wettest quarter (bio10), and precipitation of the driest quarter (bio17). To reduce multicollinearity in species distribution models for each species, we only retained climate variables with a variable inflation factor < 10. The climate variables were from the CHELSA database (https://chelsa-climate.org/), which can be freely downloaded for current and future scenarios. We also provide MCC tree files for the geckos and skinks. The phylogenetic trees have been constructed for NZ geckos by (Nielsen et al., 2011) and for NZ skinks by (Chapple et al., 2009). For geckos we used a subset of the sequences used by Nielsen et al. (2011) for four genes, two nuclear (RAG 1, PDC) and two mitochondrial (16S, ND2 along with flanking tRNA sequences). For skinks, we used sequences from Chapple et al. (2009) for one nuclear (RAG 1) and five mitochondrial (ND2, ND4, Cyt b, 12S and 16S) genes, and additional ND2 sequences for taxa not included in the original phylogeny (Chapple et al., 2011, p. 201). In total we used sequences for all recognised extant taxa (Hitchmough et al., 2016) as at 2019 except for three species of skink (O. aff. inconspicuum “Okuru”, O. robinsoni, and O. aff. inconspicuum “North Otago”) and two species of gecko (M. “Cupola” and W. “Kaikouras”) for which genetic data were not available. Aim: The primary drivers of species and population extirpations have been habitat loss, overexploitation, and invasive species, but human-mediated climate change is expected to be a major driver in future. To minimise biodiversity loss, conservation managers should identify species vulnerable to climate change and prioritise their protection. Here, we estimate climatic suitability for two speciose taxonomic groups, then use phylogenetic analyses to assess vulnerability to climate change. Location: Aotearoa New Zealand (NZ) Taxa: NZ lizards: diplodactylid geckos and eugongylinae skinks Methods: We built correlative species distribution models (SDMs) for NZ geckos and skinks to estimate climatic suitability under current climate and 2070 future-climate scenarios. We then used Bayesian phylogenetic mixed models (BPMMs) to assess vulnerability for both groups with predictor variables for life history traits (body size and activity phase) and current distribution (elevation and latitude). We explored two scenarios: an unlimited dispersal scenario, where projections track climate, and a no-dispersal scenario, where projections are restricted to areas currently identified as suitable. Results: SDMs projected vulnerability to climate change for most modelled lizards. For species’ ranges projected to decline in climatically suitable areas, average decreases were between 42–45% for geckos and 33–91% for skinks, although area did increase or remain stable for a minority of species. For the no-dispersal scenario, the average decrease for geckos was 37–52% and for skinks was 33–52%. Our BPMMs showed phylogenetic signal in climate change vulnerability for both groups, with elevation increasing vulnerability for geckos, and body size reducing vulnerability for skinks. Main conclusions: NZ lizards showed variable vulnerability to climate change, with most species’ ranges predicted to decrease. For species whose suitable climatic space is projected to disappear from within their current range, managed relocation could be considered to establish populations in regions that will be suitable under future climates.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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visibility 53visibility views 53 download downloads 15 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.d51c5b058&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | PARIS REINFORCEEC| PARIS REINFORCEDoukas, Haris; Spiliotis, Evangelos; Jafari, Mohsen A.; Giarola, Sara; Nikas, Alexandros;This dataset contains the underlying data for the following publication: Doukas, H., Spiliotis, E., Jafari, M. A., Giarola, S. & Nikas, A. (2021). Low-cost emissions cuts in container shipping: Thinking inside the box. Transportation Research Part D: Transport and Environment, 94, 102815, https://doi.org/10.1016/j.trd.2021.102815.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5666359&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 24visibility views 24 download downloads 1 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5666359&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:EC | REINFORCEEC| REINFORCEAuthors: Mina, Marco;Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.8131674&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 30visibility views 30 download downloads 2 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.8131674&type=result"></script>'); --> </script>
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