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161 results for “European forests”
Data from: Functional structure of European forest beetle communities is enhanced by rare species
<p>From article abstract:</p> <p><a href="https://doi.org/10.1016/j.biocon.2022.109491">https://doi.org/10.1016/j.biocon.2022.109491</a></p> <p><strong>ABSTRACT</strong></p> <p>Biodiverse communities have been shown to sustain high levels of multifunctionality and thus a loss of species likely negatively impacts ecosystem functions. For most taxa, however, the roles of individual species are poorly known. Rare species, often the most likely to go extinct, may have unique traits leading to unique functional roles. Alternatively, rare species may be functionally redundant, such that their loss would not disrupt ecosystem functions. We quantified the functional role of rare species by using capture records of wood-living (saproxylic) beetle species, combined with recent databases of their morphological and ecological traits, from three regions in central and northern Europe. Using a rarity index based on species’ local abundance, geographic range, and habitat breadth, we used local and regional species removal simulations to examine the contributions of both the rarest and the most common beetle species to three measures of community functional structure: functional richness, functional specialization, and functional originality. In both regional species pools and local communities, all three of these measures declined more rapidly when rare species were removed than under common (or random) species removal scenarios. These consistent patterns across scales and among several forest types give evidence that rare species provide unique functional contributions, and that their loss may disproportionately impact ecosystem functions. This implies that conservation measures targeting rare and endangered species, such as preserving intact forests with dead wood and mature trees, can provide broader ecosystem-level benefits. Experimental research linking functional structure to ecosystem processes should be prioritized to increase our understanding of the functional consequences of species loss and to develop more effective conservation strategies.</p> <p> </p> <p><strong>DATASET DESCRIPTION</strong></p> <p>This dataset includes a) beetle capture information and b) beetle trait information from three countries: 1) Norway, 2) Finland, and 3) Germany. </p> <p> </p> <p><strong>FILES</strong></p> <p><strong>readme.txt</strong> -- this has the information from this description section</p> <p><strong>Norway_traits.csv</strong>, <strong>Finland_traits.csv</strong>, <strong>Germany_traits.csv</strong> -- these are the trait files, including all species</p> <p><strong>Norway_sites.species.csv</strong>, <strong>Finland_sites.species.csv</strong>, <strong>Germany_sites.species.csv</strong> -- this has species (rows) by sites (columns); values are the number of beetles caught (for number of traps, dates, and other site covariates, see related dataset: <a href="https://doi.org/10.5061/dryad.tmpg4f50b">https://doi.org/10.5061/dryad.tmpg4f50b</a> and manuscript: <a href="https://doi.org/10.1111/jbi.14272">https://doi.org/10.1111/jbi.14272</a>). Species names follow GBIF taxonomic backbone.</p> <p><strong>Traits_METADATA.csv</strong> -- this has information on all the fields in the trait data</p> <p> </p>
Prevalent trends in realized probability of occurrence of main European forest tree species for 2000–2020
<p>High resolution maps resulting from a trend analysis conducted for the period 2000–2020 on the probability of occurrence maps prepared by <a href="https://doi.org/10.7717/peerj.13728">Bonannella et al. (2022)</a>. For this analysis we selected the realized distribution time series layers at 30m spatial resolution for 6 out of 16 species described in the mentioned publication:</p> <ul> <li>Silver fir (<em>Abies alba </em>Mill.)</li> <li>European beech (<em>Fagus sylvatica </em>L.)</li> <li>Norway spruce (<em>Picea abies </em>L.)</li> <li>Black pine (<em>Pinus nigra </em>J. F. Arnold)</li> <li>Scots pine (<em>Pinus sylvestris </em>L.)</li> <li>Common oak (<em>Quercus robur </em>L.)</li> </ul> <p>The trend analysis was conducted per pixel on each of these species individually. We fitted simple OLS regression models with the probability of occurrence as the dependent variable and time as the independent variable. After the model fitting, we also calculated the t-test statistics to determine the presence of an increasing (positive) or decreasing (negative) trend or no trend at all.</p> <p>By combining the regression slope coefficient (<em>β</em>) and the <em>p</em>-value from the t-test statistics we assigned each pixel to one of three classes:</p> <ul> <li><em>positive</em>: <em>β</em> > 0.25 AND <em>p</em>-value < 0.05</li> <li><em>negative</em>: <em>β</em> < −0.25 AND <em>p</em>-value < 0.05</li> <li><em>no trend / stable</em>: −0.25 ≤ <em>β</em> ≥ 0.25 OR <em>p</em>-value > 0.05</li> </ul> <p>We then aggregated the resulting classes at 1km resolution maps to capture the prevalent trend in probability of occurrence over a certain area. Files are named according to the following naming convention, e.g.:</p> <ul> <li>veg_abies.alba_slope_30m_0..0cm_epsg3035_v1.0</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>abies.alba</strong>,</li> <li>variable name: e.g. <strong>slope</strong>,</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v1.0</strong>.</li> </ul> <p>For each species here we provide the following layers:</p> <ul> <li>veg_abies.alba_<strong>slope</strong>:<strong> </strong>slope coefficient (scaling factor: 10000)</li> <li>veg_abies.alba_<strong>pvalue</strong>:<strong> </strong><em>p</em>-value (scaling factor: 1000)</li> <li>veg_abies.alba_<strong>pos.trends_30m</strong>: pixels classified as <em>positive </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>pos.trends_1km</strong>: proportion of pixels of the <em>positive </em>class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_<strong>neg.trends_30m</strong>: pixels classified as <em>negative </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>neg.trends_1km</strong>: proportion of pixels of the <em>negative </em>class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_<strong>no.trends_30m</strong>: (pixels classified as <em>no trend / stable </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>no.trends_1km</strong>:<strong> </strong>proportion of pixels of the <em>no trend / stable </em>class over a 1×1 km area (range 0–100)</li> </ul> <p>Files are provided as GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in <em>QML</em> format</p> <p>A publication describing, in detail, all processing steps is currently in review. See at:<br> <br> Bonannella, C., Parente, L., de Bruin, S. and Herold, M. (2023). Multi-decadal trend analysis and forest disturbance assessment of European tree species: concerning signs of a subtle shift, PREPRINT (Version 1) available at Research Square [<a href="https://doi.org/10.21203/rs.3.rs-3288937/v1">https://doi.org/10.21203/rs.3.rs-3288937/v1</a>]</p> <p> </p>
Land cover, landscape metrics and typology of European cities for Urban Forest Ecosystem Services (UFES) evaluation
<p>The data refers to the paper "<em>Urban Forests as Regulating Ecosystems: Types and Ranking of European Cities</em>"</p> <p>The datasets provide a typology for 689 European urban areas, the land cover metrics and landscape metrics used to create the typology and the Urban Forest Ecosystem Services (UFES) indexes created from them.</p> <p>The typology of Urban Forest Ecosystem Services (UFES) presents 10 clusters of cities aggregated into 4 groups: Forest cities, Anthropogenic cities, Herbaceous cities and Standard European cities. The data can be used to support urban planning policies at local and regional scales; in urban forestry, urban form and ecosystem services work related at different spatial scales. The metrics used capture the spatial integration of different layers of natural, semi-natural and artificial land within functional urban areas.</p> <p> </p> <p>The datasets are a csv file (<code>Metrics.csv</code>) and a shapefile (<code>UFES.shp</code>) of polygons with attributes.</p> <ul> <li> <p><code>UFES.shp</code> attributes' are the following: FUA codes, country name, main city name, clusters and groups of FUAs resulting from the hierarchical cluster analysis (HCA), the R color codes used in the article, the five UFES budget indexes as well as an aggregated global UFES index for each FUA.</p> </li> <li> <p><code>Metrics.csv</code> contains the FUA codes, the land cover and landscape metrics used in the HCA.</p> </li> </ul> <p> </p>
Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity
<p># Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity</p> <p>## Author<br> Marieke Scheel, Lund University, Sweden, marieke.scheel@gmail.com</p> <p>## Description<br> Data underlying analysis in:<br> Marieke Scheel, Mats Lindeskog, Benjamin Smith, Susanne Suvanto, Thomas A. M. Pugh<br> Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity<br> Scripts underlying analysis:<br> https://github.com/mariekesche/harvest_driven_canopy_mortality</p> <p>Folder: harvest_checks<br> - NFI_Germany.txt, National Forest Index data from Germany as difference between inventories in 200-2003 and 2011-2013, values are given as fraction, n: number of NFI plots in a grid cell, HARVEST_ALL: clear-cut harvest, HARVEST_PARTIAL: thinning harvest, NATDEAD: natural dead (not harvested)</p> <p>Folder: manag_climfix (S_man,clim)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_co2fix (S_man,CO2)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_ndepfix (S_man,N)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_nofix (S_man)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - closs.txt, biomass loss in kg [C]/m^2 year split into DBH classes<br> - closs_harv.txt, biomass loss due to harvest in kg [C]/m^2 year split into DBH classes<br> - cpool_forest_rel.txt, biomass of forests in kg [C]/m^2 year<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - crownloss_age.txt, m^2 of crown/m^2 of ground lost per year due to age<br> - crownloss_dist.txt, m^2 of crown/m^2 of ground lost per year due to disturbance<br> - crownloss_fire.txt, m^2 of crown/m^2 of ground lost per year due to fire disturbance<br> - crownloss_greff.txt, m^2 of crown/m^2 of ground lost per year due to growth efficiency<br> - crownloss_harv.txt, m^2 of crown/m^2 of ground lost per year due to harvest<br> - crownloss_other.txt, m^2 of crown/m^2 of ground lost per year due to other reasons<br> - crownloss_thin.txt, m^2 of crown/m^2 of ground lost per year due to natural thinning<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes<br> - diam_dens.txt, total number of trees/m^2 year split into DBH classes<br> - stemloss.txt, number of trees/m^2 year lost in respective cells<br> - stemloss_harv.txt, number of trees/m^2 year lost in respective cells due to harvest</p> <p>Folder: manag_nothin (S_nothin)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year<br> - lai.txt, leaf area index (LAI) for simulated species and plant functional types (PFTs)</p> <p>Folder: PNV (S_PNV)<br> - cflux.txt, Net Primary Production (NPP) values in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - crownloss_age.txt, m^2 of crown/m^2 of ground lost per year due to age<br> - crownloss_dist.txt, m^2 of crown/m^2 of ground lost per year due to disturbance<br> - crownloss_fire.txt, m^2 of crown/m^2 of ground lost per year due to fire disturbance<br> - crownloss_greff.txt, m^2 of crown/m^2 of ground lost per year due to growth efficiency<br> - crownloss_other.txt, m^2 of crown/m^2 of ground lost per year due to other reasons<br> - crownloss_thin.txt, m^2 of crown/m^2 of ground lost per year due to natural thinning<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: dependencies<br> - gridlist.txt, coordinates of 0.5°x0.5° grid cells that simulations were run on; longitude, latitude, FAO number, size in m^2<br> - landcover_eu.txt, input file LPJ-GUESS model showing changes from natural to forest (harvest); longitude, latitude, year, natural, forest, barren</p> <p>## Dependencies<br> - canopy mortality rates published in "Senf C Pflugmacher D Zhiqiang Y Sebald J Knorn J Neumann M Hostert P and Seidl R 2018 Canopy mortality has doubled in Europe’s temperate forests over the last three decades Nature Communications 9 4978 10.1038/s41467-018-07539-6"<br> - harvest removal rates published in "Ceccherini G Duveiller G Grassi G Lemoine G Avitabile V Pilli R and Cescatti A 2020<br> Abrupt increase in harvested forest area over Europe after 2015 Nature 583 72-77 10.1038/s41586-020-2438-y"<br> - FAO forest removal area data data retrieved from https://www.fao.org/faostat/en/#data/GF (24.07.2021), modified for overview (1st column: Area Code, 2nd column: Year, 3rd column: Area in 1000 ha)</p>
European Forest Disturbance Atlas
<p><strong>Description</strong></p> <p>This repository holds maps of annual forest disturbances across 38 European countries derived from Landsat satellite data. The European Forest Disturbance Atlas currently covers the period 1985-2023 and consists of a set of maps:</p> <ul> <li>The <em>year of disturbance</em> layers contain the year of the most recent disturbance event in the time-series, the greatest disturbance in terms of spectral change and stack of annual disturbances indicating undisturbed (0) and disturbed (1).</li> <li>The <em>number of disturbances</em> layer shows the number of disturbance events detected within the time-series.</li> <li>The <em>disturbance severity</em> layer indicates the spectral change in NBR relative to pre-disturbance.</li> <li>The <em>disturbance agent</em> layer summarises the attribution of agents over the full time series. The causal agents assigned are wind/bark beetle complex (1), fire (2), harvest (3) and mixed agents (4, where more than one agent occurred). The stack of disturbance agents provides annual information on causal agent assigned.</li> </ul> <p>The maps are available per country as GeoTIFF. The spatial reference system is EPSG 3035 (ETRS89 / LAEA Europe). The most current version is 2.1.1. The maps will be updated regularly. <a href="https://albaviana.users.earthengine.app/view/european-forest-disturbance-map">The maps can also be explored online</a>.</p> <p><strong>Version history</strong></p> <ul> <li>2.0.0 - Initial version, covering 1985-2021.</li> <li>2.1.0 - Maps updated until 2023. Added forest land use layer and annual stacks of disturbances (including annual disturbance probabilities and disturbance agents).</li> <li>2.1.1 - Improvements to the disturbance maps introduced and disturbance severity layer added.</li> </ul> <p><strong>Known issues</strong></p> <ul> <li>Some SLC-off artefacts (both version 2.0.0 and 2.1.0).</li> <li>Known errors related to the forest mask of Belarus, Moldova and Ukraine (now corrected in 2.1.0)</li> <li>Known some false disturbances mapped north of 67°N in Norway, Sweeden and Finland.</li> </ul>
Climate targets in European timber-producing countries conflict with goals on forest ecosystem services and biodiversity
<p>The repository contains the data and codes supporting the findings of the study:<strong> </strong>Climate targets in European timber-producing countries conflict with goals on forest ecosystem services and biodiversity, which can be found in the zip file "<strong>euclimate_vs_natpolicy-main.zip"</strong>. </p> <p>Further, the repository includes the raw forest simulation data used as input for the multi-objective optimizations and the raw optimization outputs of each study region. The codes to run the national optimization can be retrieved from <a href="http://doi.org/10.5281/zenodo.6631109">https://doi.org/10.5281/zenodo.6631109</a>.</p> <p>Abstract:</p> <p>The European Union (EU) set clear climate change mitigation targets to reach climate neutrality, accounting for forests and their woody biomass resources. We investigated the consequences of increased harvest demands resulting from EU climate targets. We analysed the impacts on national policy objectives for forest ecosystem services and biodiversity through empirical forest simulation and multi-objective optimization methods. We show that key European timber-producing countries – Finland, Sweden, Germany (Bavaria) – cannot fulfil the increased harvest demands linked to the ambitious 1.5°C target. Potentials for harvest increase only exists in the studied region Norway. However, focusing on EU climate targets conflicts with several national policies and causes adverse effects on multiple ecosystem services and biodiversity. We argue that the role of forests and their timber resources in achieving climate targets and societal decarbonization should not be overstated. Our study provides insight for other European countries challenged by conflicting policies and supports policymakers.</p>
The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems: scripts, model output, and parameter files
<p>This repository contains the model outputs and R scripts used to process the data to analyze the impact of the plant hydraulic parameterization of the manuscript: "The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems". The following is a detailed description of the content of this repository:</p> <p>model_output.zip: This compressed file contains the results of all the individual numerical experiments per experimental site as produced by the Comunity Land Model version 5. The files are stored in NETCDF format per year. The folder is arranged with subfolders containing the individual results from each experimental site as follows:</p> <ul> <li>rc: model output with the results of the resistant configuration of experiment 1 (RC)</li> <li>vc: model output with the results of the vulnerable configuration of experiment 1 (VC)</li> <li>k_dc: model output with the results of the default configuration used for experiments 1 and 2 (DC or DC<em>k</em><sub>max</sub>)</li> <li>k_rc: model output with the results of the low plant hydraulic conductance (L<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_irc: model output with the results of the intermediate low plant hydraulic conductance (IL<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_vc: model output with the results of the high plant hydraulic conductance (H<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_ivc: model output with the results of the intermediate high plant hydraulic conductance (IH<em>k</em><sub>max</sub>) for experiment 2</li> <li>k_iirc: model output with the results of the additional intermediate low plant hydraulic conductance (IIL<em>k</em><sub>max</sub>) for experiment 2</li> <li>ko_dc: model output with the results of the best <em>k</em><sub>max</sub> and the default configuration of the PVC used in experiment 3</li> <li>ko_rc: model output with the results of the best <em>k</em><sub>max</sub> and the resistant configuration of the PVC used in experiment 3</li> <li>ko_vc: model output with the results of the best <em>k</em><sub>max</sub> and the vulnerable configuration of the PVC used in experiment 3</li> </ul> <p>The scripts were written for use in RStudio, and each contains a detailed description of the data requirements and outputs. Each script was developed to read directly the netcdf files of the model output and the csv files containing the transpiration estimates calculated from the SAPFLUXNET per experimental site (script 1).</p>
Dataset for "Phylogenetic structure of European forest vegetation" - Journal of Biogeography (DOI: 10.1111/jbi.14046)
<p>This dataset contains the list of plant occurrences and geographical and environmental attributes of the vegetation-plots analyzed in the paper titled “Phylogenetic structure of European forest vegetation” by Padullés Cubino et al. (2021; Journal of Biogeography; DOI: 10.1111/jbi.14046). </p> <p>The dataset contains 3 tables:</p> <ol> <li>“Table_taxa.csv”: It includes the list of angiosperm plant taxa in selected vegetation plots.</li> <li>“Table_sites.csv”: It includes data on the environmental variables of plots, their classification into different forest types, their location in 1<sup>o</sup> × 1<sup>o</sup> grid cells, and the reference to the original datasets archived in the European Vegetation Archive (EVA; http://euroveg.org/eva-database-participating-databases).</li> <li>“Metadata.csv”: It includes a description of the fields found in the two previous tables.</li> </ol>
Рис. 4. МестонахожΑение Chrysopa viridinervis Jakowleff, 1869 на опушке ХваΛынского Λеса, Саратовская обΛасть. Fig. 4. The locality of Chrysopa viridinervis Jakowleff, 1869 at the edge of the Khvalynsk Forest, Saratov Region. in New data on Neuropterida from the southern part of the European Russia
Рис. 4. МестонахожΑение Chrysopa viridinervis Jakowleff, 1869 на опушке ХваΛынского Λеса, Саратовская обΛасть. Fig. 4. The locality of Chrysopa viridinervis Jakowleff, 1869 at the edge of the Khvalynsk Forest, Saratov Region.
Data: Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality
<p><strong>The repository contains the data supporting the findings of the study: <em>Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality</em></strong></p> <p><strong>Abstract:</strong></p> <p>Close-to-nature forestry (CNF) has a long tradition in European Alpine forest management, playing a crucial role in ensuring the continuous provision of biodiversity and forest ecosystem services, including protection against natural hazards. However, climate change is causing huge uncertainties about the future applicability of CNF in the Alpine region. The question arises as to whether current CNF practices are still suitable for adapting forests to climate change impacts while also meeting the increasing societal demands regarding Alpine forests, including their potential contribution to climate change mitigation.</p> <p>To answer this question, we simulated forest development using the ForClim forest model at two Alpine study sites, together representing a large biogeographic gradient from high-elevation inner Alpine forests (Switzerland) to lower-elevation south-eastern Alpine forests (Slovenia). The simulations considered three climate scenarios (historical climate, SSP2‑4.5 and SSP5-8.5) and six alternative management strategies, including both current CNF management practices and climate-adapted versions. Using a multi-criteria decision analysis framework, we assessed the joint impacts of climate and management on biodiversity and key ecosystem services of the investigated regions, including carbon sequestration (CS) inside and outside the forest ecosystem boundary. </p> <p>The joint effects of climate change and CNF varied, both among and within the study sites along the biogeographical gradient. While CS was more resistant to climate change under current CNF at the south-eastern Alpine site, it was more sensitive at the inner Alpine site, where CS potentials decreased at lower elevations. This adverse effect could be partly mitigated by fostering the use of climate-adapted tree species. However, current CNF and adaptations of it did not meet multiple management objectives equally well: while protection from gravitation hazards and timber production also benefited from this silvicultural practice, biodiversity benefited from CNF variants with low-intensity or no management. </p> <p>In conclusion, CNF has a high potential to continue fulfilling its crucial role in European Alpine forests. A differentiated approach will be needed in the future, however, to identify forest stands where adaptive measures are required, especially at sites particularly vulnerable to climate change. In combination with less intensively managed or unmanaged areas, CNF provides a management portfolio that will help European Alpine forests to meet the demands of future society.</p> <p><strong>Data:</strong></p> <p>There is one folder for each case study, including: </p> <ul> <li>simulated biodiverstiy and ecosystem service indicators</li> <li>forest stand metadata</li> <li>normlized utility values for indicators</li> <li>partial utility values for biodiversity and ecosystem service groups</li> </ul> <p>This study was conducted as part of the <strong>ONEforest project</strong>, which received funding from the <strong>European Union's Horizon 2020</strong> research and innovation programme under the <strong>grant agreement Nº 101000406</strong>.</p>
Digital repository for: Large-scale forest disturbance and associated management shape bird communities in Central European spruce forests
<p>Repository containing R-script and data to reproduce analysis and main figures on the effect of large-scale forest disturbance and associated pre- and post-disturbance management on bird communities in the Harz Mountains, Germany.</p> <p>R-script includes:</p> <ul> <li>indicator species analysis (R package indicspecies; Cáceres & Legendre, 2009)</li> <li>non-metric multidimensional scaling (R package vegan; Oksanen et al., 2016)</li> <li>rarefaction- and extrapolation of Hill numbers (R package iNEXT; Hsieh et al., 2019)</li> <li>multi-species community distance sampling (R package sp Abundance; Doser et al., 2023)</li> </ul> <p>Attached files:</p> <ul> <li><strong>bird_data_Graser_et_al.csv </strong>(row data of bird species point counts per distance category)</li> <li><strong>bird_data_abundance_100_Graser_et_al.csv </strong>(abundance of species per sampling site, summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>siteCovs_Graser_et_al.csv</strong> (environmental variables for each sampling point)</li> <li><strong>A_species_matrix_100_new_Graser_et_al.csv</strong> (species-site matrix of <strong>bark-beetle disturbance, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>B_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>windthrow disturbance, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>C_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle/windthrow disturbance, underplanted, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>D_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle /windthrow disturbance, salvage-unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>E_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle /windthrow disturbance, underplanted, salvage-unlogged </strong>sites for rarefaction and extrapolation, summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong> F_species_matrix_100_new_Graser_et_al.cs</strong>v (species-site matrix of <strong>mature spruce plantation </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>msHDS_bird_data_management_model_Graser_et_al.rds</strong> (R-data set for multi-species community distance sampling of the effect of different pre- and post-disturbance management groups)</li> <li><strong>msHDS_bird_data_stand_age_model_Graser_et_al.rds </strong>(R-data set for multi-species community distance sampling of the effect of post-disturbance forest succession)</li> </ul> <p>A more detailed description of the data can be found in the README.txt document.</p> <p><span>References:</span></p> <p><span>Cáceres, M. D., & Legendre, P. (2009). </span><span>Associations between species and groups of sites: Indices and statistical inference. <em>Ecology</em>, <em>90</em>(12), 3566–3574. https://doi.org/10.1890/08-1823.1</span></p> <p><span>Doser, J. W., Finley, A. O., Kéry, M., & Zipkin, E. F. (2023). spAbundance: An R package for single‐species and multi‐species spatially explicit abundance models. <em>Methods in Ecology and Evolution</em>, <em>15</em>(6), 1024–1033. https://doi.org/10.1111/2041-210X.14332</span></p> <p><span>Hsieh, T. C., Ma, K. H., & Chao, A. (2019). <em>iNEXT-package: Interpolation and extrapolation for species diversity</em>. https://cran.r-project.org/web/packages/iNEXT/vignettes/Introduction.html</span></p> <p><span>Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., O’hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Wagner, H., Minchin, P. R., Gavin, L., & Henry, H. (2016). Vegan: Community ecology package. R package version 1.17-4. <em>Http://CRAN. R-Project. </em></span><em><span>Org/Package=vegan</span></em><span>.</span></p> <p></p> <p></p>
Data from: The 2018 European heatwave led to stem dehydration but not to consistent growth reductions in forests
<p>Heatwaves exert disproportionately strong and sometimes irreversible impacts on forest ecosystems. These impacts remain poorly understood at the tree and species level and across large spatial scales. Here, we investigate the effects of the record-breaking 2018 European heatwave on tree growth and tree water status using a collection of high-temporal resolution dendrometer data from 21 species across 53 sites. Relative to the two preceding years, annual stem growth was not consistently reduced by the 2018 heatwave but stems experienced twice the temporary shrinkage due to depletion of water reserves. Conifer species were less capable of rehydrating overnight than broadleaves across gradients of soil and atmospheric drought, suggesting less resilience toward transient stress. In particular, Norway spruce and Scots pine experienced extensive stem dehydration. Our high-resolution dendrometer network was suitable to disentangle the effects of a severe heatwave on tree growth and desiccation at large-spatial scales in situ, and provided insights on which species may be more vulnerable to climate extremes.</p>
Fig. 4 in Home Ranges Of The Red Fox, Vulpes Vulpes (Carnivora, Canidae) And European Badger, Meles Meles (Carnivora, Mustelidae), In Oak Forests Of Slobozhanshchyna, Ukraine
Fig. 4. Winter average monthly temperatures in Kharkiv City in 2007–2009 and 2011 (http://www.pogodaiklimat.ru).
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Silvicultural regime shapes understory functional structure in European forests
<p>This is the dataset used for the article "Silvicultural regime shapes understory functional structure in European forests" by Francesco Chianucci, Francesca Napoleone et al., which has been accepted in Journal of Applied Ecology.</p> <p>Attached is also an R code to illustrate the statistical analyses performed in the study.</p>
Fig.6 in Reaching The Climate Objectives Of Forest Management In Latvia Within The Scope Of European Climate Policy
Fig.6. CO2 emissions in Latvia by sources in 2016, CO2 kt eq (Source: Created by authors after National GHG inventories).
Fig.2 in Reaching The Climate Objectives Of Forest Management In Latvia Within The Scope Of European Climate Policy
Fig.2. Scheme of LFSRI Silava changes in forest resources projections process based on Nation Forest Inventory data (Source: LFSRI Silava).
Fig.1 in Reaching The Climate Objectives Of Forest Management In Latvia Within The Scope Of European Climate Policy
Fig.1. Policy documents affecting the Climate Change Mitigation policy of the Forest sector by 2020.
Sensitivity of fire weather indices, fuel sticks and satellite observations to fuel moisture content in Central European forests - Data
<p>This data repository contains datasets for destructively measured fuels of different types (FMC_insitu), meteorological data including 10-hour fuel stick measurements (FWS_30min) and calculated fire weather index components (FWS_FWI_24h) for four different sites in the Tharandt forest and Saxon Switzerland National Park in the Free State of Saxony (Germany) during the years 2022 (only DE-Tha) and 2023 (all four sites).</p> <p>The provided folders contain .csv files for each study site. Meteorological data in 30min for DE-Tha can be derived from the ICOS data portal (https://data.icos-cp.eu/portal/). For the remaining three sites (DE-BLB, DE-BWB, DE-SHW), past and recent data can be viewed via EMS Brno (e.g., http://www.emsbrno.cz/p.axd/en/Beech__Landberg.TU__DRESDEN.html). Upon request, the authors can share the data. </p> <p><strong>FMC_insitu</strong>: Destructively sampled fuel moisture content of different fuel types.</p> <p><strong>FWS_30min</strong>: Original measurements from the fire weather stations in 30 min time steps</p> <p><strong>FWS_FWI_24h</strong>: Measurements from fire weather stations in 24h time steps and the calculated fire weather index and its components. As requested for calculation of the FWI, meteorological variables are used at 13:00 (UTC), while PREC and PBC are the 24h sum prior to 13:00. </p> <p><strong>readme.txt</strong>: Description of repository content and the variables provided within the .csv files.</p> <p><strong>stations.csv</strong>: Contains the coordinates and a short description of the study sites. </p>
Data from: Spatial configuration matters when removing windfelled trees to manage bark beetle disturbances in Central European forest landscapes
<p>The published dataset contains the result of the paper titled <strong>Spatial configuration matters when removing windfelled trees to manage bark beetle disturbances in Central European forest landscapes, in Journal of Environmental Management.</strong></p> <p>Two zip files contain maps in ascii format for the total bark beetle and wind damage over 54 simulation year in our study region in Slovakia under reference climate and different climate change scenarios. No- salvaging and 95% salvaging scenarios are shown, just as in the paper.</p> <p>Excel file contains data of the other figures in the paper (main text and appendices as well).</p> <p>See more details about target area, and iLand model:</p> <p>https://www.sciencedirect.com/science/article/pii/S0168192318302946</p> <p>http://iland.boku.ac.at/startpage</p> <p>contact: Laura Dobor; dobor.laura@gmail.com</p> <p> </p>
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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
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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.