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113 results for “Forestry”
Towers Forestry Plot, Long-term Vegetation Monitoring in a 1-ha old-growth Rainforest, La Selva Research Station, OTS, Sarapiquí, Heredia, Costa Rica, 2010–2020
The Towers Plot is a 1-hectare permanent vegetation plot established in 2010 under the canopy towers at La Selva Research Station, Sarapiquí, Heredia, Costa Rica. The plot was created by the Organization for Tropical Studies (OTS) to monitor long-term changes in forest structure, composition, and dynamics in an old-growth tropical rainforest. All woody stems with a diameter at breast height (DBH) of 10 cm or greater—including trees, palms, and lianas—were tagged, mapped, and measured following standardized procedures. Censuses were conducted between 2010 and 2020 to document growth, mortality, and recruitment. The dataset includes taxonomic identifications, stem diameter measurements, spatial coordinates within the plot, and metadata describing field methods and species composition. The plot was established beneath three canopy towers that had been previously constructed through the NSF-funded Major Research Instrumentation (MRI) project, NSF 0722741, which provided key infrastructure for canopy and environmental research at La Selva. This proximity created a valuable opportunity to integrate vegetation monitoring with existing environmental instrumentation. Johana Hurtado, coordinator of the Tropical Ecology, Assessment and Monitoring (TEAM) project at La Selva, collaborated with OTS staff in the establishment of the plot, ensuring methodological consistency with other tropical forest monitoring sites. This dataset provides a comprehensive record of woody plant diversity and forest structure in a lowland old-growth Neotropical rainforest. It supports research on forest dynamics, carbon storage, and ecosystem change. The overall monitoring project is ongoing; this data package contains observations from 2010 through 2020.
Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland, Natural Resources Institute Finland
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</strong></p><p><strong>Creators: Larmola T, Anttila J, Alm J </strong></p><p>The dataset consists of peat properties in a subsample of 30 drained peatland forests in Finland selected from the permanent sample plots of the 8th National Forest Inventory (systematic sample of plots on drained peatland forests, e.g., Hotanen et al. 2006). The subsample included equally different site types of forestry-drained peatlands of those parts of Finland where drainage for forestry is economically viable (Latitude 60-66 ºN, annual temperature sum > 750 dd). </p><p><strong>The site selection criteria</strong> were average peat layer thickness of over 20 cm, no clear-cut areas, site drained before 1995 and ditching had detectably altered hydrology or vegetation. <strong>Peat analyses</strong> Finnish Forest Research Institute (now Natural Resources Institute Finland) sampled peat cores with a box corer in 2002, samples were analysed for bulk density, archived and remaining samples at depths 20-30, 30-40 cm (total of 58) were analysed in 2021. The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. </p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola, T. Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Version 1) [Dataset]. Zenodo. doi.org/<strong>10.5281/zenodo.10068486</strong></p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description</strong></p><p>ID - Site identifier</p><p>site - Forestry-drained peatland (FDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 30: 20-30 cm, 40: 30-40cm, avg: average of both depths.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin – Origin of the FDP site type at undrained state. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p>n - Number of samples. 2 for averages from both depths, 1 for all other rows.</p><p> </p><p><strong>References</strong></p><p>Hotanen JP, Maltamo M, Reinikainen A (2006) Canopy stratification in peatland forests in Finland. Silva Fennica 40:53–82.</p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p><p> </p>
Historical Animal Observation Records by Bavarian Forestry Offices (1845)
<p>In 1845, under the scientific direction of Andreas Wagner, the Bavarian government recorded the occurrence of 44 selected vertebrate species across the entire country. To this end, Wagner had a survey questionnaire sent to all 119 forestry offices in the state. The foresters' responses were now systematically recorded and analyzed for the first time. This data set represents the result of this survey. Among other things, it contains 5,467 geo-coded animal observation data.</p> <p>The data is the result of an interdisciplinary collaboration between scientists from the Chair of Computational Humanities at the University of Passau, the Directorate General of the Bavarian State Archives Munich, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, the Center for Biodiversity Informatics and Collection Data Integration at the Botanical Garden Berlin, and the NFDI4Biodiversity consortium.</p>
DESIRA - inventory of digital tools for agriculture, forestry, and rural areas
<p>Inventory of digital tools for agriculture, forestry, and rural areas collected by the DESIRA consortium.</p>
Forestry roads in the Purapel fluvial catchment and related changes in sediment connectivity
<p>This dataset contains georeferenced data of forestry roads and sediment connectivity in the Purapel catchment, which drains the Chilean Coastal Range. The forestry road network consists of all the dirt and gravel roads mapped in QGIS by observing open satellite images and vectorial data available during January 2021. The observed data are maps that were listed in the QGIS OpenLayers plugin (<a href="https://github.com/sourcepole/qgis-openlayers-plugin">https://github.com/sourcepole/qgis-openlayers-plugin</a>), such as Google Satellite (Map data ©2015 Google) and OpenStreetMap <sup>1</sup>, the road network of the Chilean Congress National Library (<a href="https://www.bcn.cl/siit/mapas_vectoriales">https://www.bcn.cl/siit/mapas_vectoriales</a>) and compositions of Sentinel 2 images (European Space Agency, courtesy of the U.S. Geological Survey) of the post-2017 fire period.</p> <p>Sediment Connectivity maps were calculated on a 5 m resolution LiDAR DTM using the Connectivity Index<sup> 2</sup>. The maps were derived from the stand-alone, free and open-source executable SedInConnect 2.3<sup> 3</sup> using the Weighting factor of <sup>2</sup> and two different targets, which are available as tif files:</p> <ul> <li>ICs.tif contains <em>IC<sub>s</sub></em>, the Connectivity Index to the stream network.</li> <li>ICrs.tif contains <em>ICr<sub>s</sub></em>, the Connectivity Index to the road and the stream network.</li> </ul> <p>Here, the Road Connectivity, <em>RC </em>(dimensionless) is defined as the difference between both previous maps, with the aim to describe the change in sediment connectivity due to forestry road network:</p> <ul> <li><em>RC = IC<sub>rs</sub> - IC<sub>s</sub></em></li> </ul> <p>It is available as RC.tif file. The area of<em> high RC </em>was defined using the percentile 95 (3.12). File RC95.tif is a mask of <em>RC </em><em>≥</em><em> 3.12</em>.</p> <p>The contributing area <em>CA </em>(m<sup>2</sup>) was calculated using the multiple flow D-infinity approach <sup>4</sup> using TauDEM (https://hydrology.usu.edu/taudem/taudem5/downloads.html).</p> <p>The file CA_RC95.tif contains the contributing area (m<sup>2</sup>) of the surfaces with highest changes in sediment connectivity due to the road network. That is:</p> <ul> <li><em>CA_RC95 = </em>{<em>CA </em>|<em> RC </em><em>≥</em><em> 3.12</em>}</li> </ul> <p>The landscape distribution of those surfaces, in terms of proximity to the hilltops and valleys, is described by the density plot of the raster file CA_RC95.tif in R:</p> <pre><code>library("raster") library("ggplot2") CA_RC95<-raster("CA_RC95.tif") CA_RC95<-CA_RC95*0.0025 df = as.data.frame(CA_RC95) df = na.omit(df) ggplot(df,aes(CA_RC95)) + geom_histogram(aes(y=..count..*25),binwidth = 50)+ geom_density(aes(y=50 * ..count..*25), col="blue",size=2, adjust=10000)+ xlab("Contributing Area [ha] \n Hilltop Valley") + ylab("Area [m2]")+ theme(axis.text.x = element_text(face="bold", size=30), plot.title = element_text(color="black", size=40, face="bold",hjust=0.5), axis.title.x=element_text(color="blue", size=40, face="bold"), axis.text.y = element_text(face="bold", size=30), axis.title.y=element_text(color="blue", size=40, face="bold"))+ scale_y_continuous(trans = 'log10')+ ggtitle("Upstream area of surfaces with \n High Road Connectivity (RC > 3.12)") </code></pre> <p>Bibliography</p> <p>1. OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org/ (2017).</p> <p>2. Cavalli, M., Trevisani, S., Comiti, F. & Marchi, L. Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. <em>Geomorphology</em> <strong>188</strong>, 31–41 (2013).</p> <p>3. Crema, S. & Cavalli, M. SedInConnect: a stand-alone, free and open source tool for the assessment of sediment connectivity. <em>Computers and Geosciences</em> <strong>111</strong>, 39–45 (2018).</p> <p>4. Tarboton, D. G. A new method for the determination of flow directions and upslope areas in grid digital elevation models. <em>Water Resources Research</em> <strong>33</strong>, 309–319 (1997). </p>
Tree diameter growth and increment core δ13C data from a recently thinned forestry-drained site (Lettosuo) in southern Finland.
<p>Dataset includes increment core data from Lettosuo drained peatland forest site. The study site locates in the Tammela municipality in southern Finland (60° 38’ 31’’ N, 23° 57’ 35’’ E). Increment cores were analysed for the ring widths for dominant and suppressed Norway spruce trees, and for the ring δ<sup>13</sup>C values from suppressed Norway spruce trees. Data was collected as a part of BiBiFe (”Biogeochemical and biophysical feedbacks from forest harvesting to climate change”) consortium that is funded by the Academy of Finland. </p> <p> </p> <p>Sampling for increment cores was done during October 2020 for sample trees (10 in total, of which 5 were suppressed trees from thinned area and 5 suppressed trees from control area) and additional sampling was conducted for annual diameter increment for 3 tree groups to increase sample size for diameter growth (suppressed trees in thinned area [n=20], dominant trees in thinned area [n=22] and suppressed trees in control area[n=20]) during March 2021. </p> <p> </p> <p><strong>Tree </strong><strong>ring carbon isotope data</strong></p> <p> </p> <p>Laser ablation IRMS method was applied in the Stable Isotope Laboratory of Luke (SILL) to quantify δ<sup>13</sup>C values in 10 increment cores for the time period 2010–2020, following principles of Schulze et al. (2004) and described in Lehtonen et al (manuscript). Up to 11 evenly spaced “spots” for each annual tree ring were measured to obtain information on the intra-annual variation of δ<sup>13</sup>C of the samples. </p> <p> </p> <p>(1) File: Lettosuo_d13C.xls</p> <p>File includes d13C measurements</p> <p> </p> <p><strong>Data column description below for isotope data: </strong></p> <p> </p> <p><strong>id</strong> stands for tree id [id includes tree identity, year and also spot number]</p> <p><strong>year</strong> is the year of the tree ring</p> <p><strong>nr</strong> is an index for data </p> <p><strong>tree</strong> indicates tree identity "C" for control and "H" for harvest</p> <p><strong>treatment</strong> indicates the treatment of the sampling area (control / harvest)</p> <p><strong>d13C</strong> gives the measured d13C value based on the LA-IRMS measurements</p> <p><strong>season </strong>indicates whether observation originated from the earlywood (EW) or latewood (LW) period, where 1 is EW and 2 is LW</p> <p> </p> <p><strong>Tree ring width measurements</strong></p> <p> </p> <p>In addition to the δ<sup>13</sup>C values, also the ring widths were measured. Here, also additional dominant trees were measured. </p> <p> </p> <p>(3) Files:</p> <p>controlRW.csv</p> <p>dominantRW.csv</p> <p>thinningRW.csv</p> <p> </p> <p>Files include increment core data (in micrometers) from isotope sample trees and additional increment core trees from the control area and harvested area of the site. Dominant trees were measured only from the thinned area. </p> <p> </p> <p>In the .csv files individual columns are for ring widths for individual trees. In the controlRW.csv and thinningRW.csv files first 5 columns include diameter increments from sample trees (those that have also d13C measurements).</p> <p> </p> <p><strong>References:</strong></p> <p> </p> <p>Lehtonen A, Leppä K, Sahlstedt E, Schiestl-Aalto P, Heikkinen J, Young G, Korkiakoski M, Peltoniemi M, Rinne-Garmston K, Sarkkola S, Lohila A, Mäkipää R (manuscript). Fast recovery of Norway spruce trees after thinning from above on a drained peatland forest site.</p> <p> </p> <p>Korkiakoski M, Ojanen P, Penttilä T, Minkkinen K, Sarkkola S, Rainne J, Laurila T, Lohila A (2020) Impact of partial harvest on CH<sub>4</sub> and N<sub>2</sub>O balances of a drained boreal peatland forest. Agric For Meteorol 295:108168.</p> <p> </p> <p>Schulze B, Wirth C, Linke P, Brand WA, Kuhlmann I, Horna V, Schulze E-D (2004) Laser ablation-combustion-GC-IRMS--a new method for online analysis of intra-annual variation of 13C in tree rings. Tree Physiol 24:1193–1201.</p>
Agriculture - Forestry 5
<p>Over the last 5 years several REDD+ pilot projects were implemented in Tanzania to generate information on forest carbon stocks to contribute to the REDD+ strategy. The generated information constitutes a good source of important biodiversity data that can be used in conservation and sustainable development. Lyimo P, Munishi P, Gideon H (2018). Tree species Occurrence Data of Coastal Miombo in Western Tanzania.. Version 1.6. Sokoine University of Agriculture, Department of Ecosystems and Conservation. Occurrence dataset <a href="https://doi.org/10.15468/nyhbez">https://doi.org/10.15468/nyhbez</a> accessed via GBIF.org</p>
Agriculture - Forestry 3
<p>Miombo is the vernacular word for Brachystegia, a genus of tree comprising a large number of tree species together with Julbernadia species in woodlands. Miombo woodland is classified in the tropical and subtropical grasslands, savannas, and shrublands biome. The biome includes four woodland savanna ecoregions characterized by the predominant presence of miombo species, with a range of climates from humid to semi-arid, and tropical to subtropical or even temperate. Lyimo P, Munishi P (2018). A Checklist of Tree Species in the Miombo Woodlands of Western Tanzania. Version 1.1. Sokoine University of Agriculture, Department of Ecosystems and Conservation. Checklist dataset <a href="https://doi.org/10.15468/zofuvf">https://doi.org/10.15468/zofuvf</a> accessed via GBIF.org</p>
Agriculture - Forestry 4
<p>The goal of the project was to compile metadata of biodiversity data generated from REDD+ Pilot Projects in Tanzania. Specifically to develop data collection tool to capture the required data, inventory of REDD+ data holders and the type of data they hold, convene IPT-training workshop for holders of biodiversity data from REDD+ project to enhance publishing. Lyimo P, Munishi P, Gideon H (2018). The Occurrence Data of Tree species for Coastal Forest of Tanzania.. Version 1.5. Sokoine University of Agriculture, Department of Ecosystems and Conservation. Occurrence dataset <a href="https://doi.org/10.15468/3v0exk">https://doi.org/10.15468/3v0exk</a> accessed via GBIF.org</p>
"Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest" -- data sets
<p>These files contain the data used in the analysis and production of graphs reported in a manuscript titled "Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest," by Stefan Gronsdahl, R. Dan Moore, Jordan Rosenfeld, Rich McCleary, Rita Winkler. The paper will be published in the journal Hydrological Processes. The file named "readme.txt" explains the contents of the files.</p>
Interviews with experts on digitalisation in agriculture, forestry, and rural areas (H2020 DESIRA project, WP1)
<p>Interviews with experts on digitalisation in agriculture, forestry, and rural areas (H2020 DESIRA project, WP1).</p> <p>The scripts and the answers are provided. Two groups of experts have been interviewed: the first group with expertise in ICT, and the second group with expertise in socio-economic aspects. </p>
Lake ice surveys, 1874-2022, Adirondack Long-Term Ecological Monitoring Program Project No. 8 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
The objective of this dataset is to document ice-in and ice-out dates on several lakes on the State University of New York College of Environmental Science and Forestry's Huntington Wildlife Forest (HWF). Lakes include: Arbutus, Catlin, Deer, Military, Rich, Wolf and Lodo Pond; some records exist for Long Pond and other water bodies but they are not included here except in some comment fields.
Ecological Forestry in Western Oregon: A Critical Analysis from Andrews Forest LTER Research, 2014-2015
This work highlights the normative dimensions of “ecological forestry,” a strategy of forest management that uses silviculture to mimic the effects of non-anthropogenic processes of disturbance and succession in order to meet multiple objectives on a single piece of land. An analysis of the arguments made about ecological forestry, both broadly theoretical and pertaining specifically to western Oregon, shows that empirical uncertainties and normative gaps need to be addressed before we can make a clear, well-reasoned decision about whether ecological forestry is a viable and appropriate strategy for forest management and conservation.
Рис. 1–4. Pterostichus (Petrophilus) magoides, общий виΔ. 1–2 – самки: 1 – гоΛотип, 2 – паратип; 3–4 – самцы: 3 – из окрестностей оз. МаркакоΛь (Казахстан), 4 – из Рахмановского Λесничества (Казахстан). Figs 1–4. Pterostichus (Petrophilus) magoides, general view. 1–2 – females: 1 – holotype, 2 – paratype; 3–4 – males: 3 – from the Markakol Lake vicinities (Kazakhstan), 3 – from the Rakhmanovskoe Forestry (Kazakhstan). in To the systematic position of Pterostichus (Petrophilus) magoides (Straneo, 1937) (Coleoptera: Carabidae) from the Altai Mountains
Рис. 1–4. Pterostichus (Petrophilus) magoides, общий виΔ. 1–2 – самки: 1 – гоΛотип, 2 – паратип; 3–4 – самцы: 3 – из окрестностей оз. МаркакоΛь (Казахстан), 4 – из Рахмановского Λесничества (Казахстан). Figs 1–4. Pterostichus (Petrophilus) magoides, general view. 1–2 – females: 1 – holotype, 2 – paratype; 3–4 – males: 3 – from the Markakol Lake vicinities (Kazakhstan), 3 – from the Rakhmanovskoe Forestry (Kazakhstan).
Рис. 5–10. Pterostichus (Petrophilus) magoides, этикетки и генитаΛии самца. 5 – этикетки гоΛотипа; 6 – этикетки паратипа; 7 – энΔофаΛΛус, виΔ сΛева (экземпΛяр из Рахмановского Λесничества, Казахстан); 8–9 – меΔиаΛьная ΔоΛя эΔеагуса (экземпΛяр с Курчумского хребта, Казахстан): 8 – виΔ сΛева, 9 – виΔ сверху; 10 – правая парамера, виΔ сбоку (этот же экземпΛяр). Figs 5–10. Pterostichus (Petrophilus) magoides, labels and male genitalia. 5 – labels of the holotype; 6 – labels of the paratype; 7 – endophallus, left view (specimen from the Rakhmanovskoe Forestry, Kazakhstan); 8–9 – median lobe of aedeagus (specimen from the Kurchum Mountain Range, Kazakhstan): 8 – left view, 9 – dorsal view; 10 – right paramere, lateral view (the same specimen). in To the systematic position of Pterostichus (Petrophilus) magoides (Straneo, 1937) (Coleoptera: Carabidae) from the Altai Mountains
Рис. 5–10. Pterostichus (Petrophilus) magoides, этикетки и генитаΛии самца. 5 – этикетки гоΛотипа; 6 – этикетки паратипа; 7 – энΔофаΛΛус, виΔ сΛева (экземпΛяр из Рахмановского Λесничества, Казахстан); 8–9 – меΔиаΛьная ΔоΛя эΔеагуса (экземпΛяр с Курчумского хребта, Казахстан): 8 – виΔ сΛева, 9 – виΔ сверху; 10 – правая парамера, виΔ сбоку (этот же экземпΛяр). Figs 5–10. Pterostichus (Petrophilus) magoides, labels and male genitalia. 5 – labels of the holotype; 6 – labels of the paratype; 7 – endophallus, left view (specimen from the Rakhmanovskoe Forestry, Kazakhstan); 8–9 – median lobe of aedeagus (specimen from the Kurchum Mountain Range, Kazakhstan): 8 – left view, 9 – dorsal view; 10 – right paramere, lateral view (the same specimen).
Data for: Specialist carabids in mixed montane forests are positively associated with biodiversity-oriented forestry and abundance of roe deer
<p>The ongoing transition within forest management towards more biodiversity-oriented practices, such as close-to-nature forestry and retention forestry, may benefit forest fauna such as forest-specialized ground beetles (Coleoptera: Carabidae). However, it remains unclear how forest carabids are jointly affected by these practices in Central European montane forests, which host particularly sensitive, range-restricted carabid species, and where biodiversity-oriented forestry is widely applied. Moreover, roe deer (<em>Capreolus capreolus</em>), the most common large herbivore in these forests, is intensively managed to reduce browsing pressure, but it is yet unknown how this may affect carabids, alongside the effect of silviculture. On 66 1-ha plots in the Black Forest region of Germany, we sampled carabids with pitfall traps, measured roe deer abundances using camera trapping, and measured several structural variables directly related to close-to-nature and retention practices, as well as variables describing microclimate and landscape-level forest cover. We found that the carabid assemblage was dominated by forest specialists, with little influence from fragmentation of the surrounding forest. Higher broadleaf share (and canopy cover for montane specialists) was correlated with higher carabid activity-density. Increasing stand maturity (and lying deadwood volume for montane specialists), was correlated with higher species richness. Plots with higher roe deer abundances showed higher carabid richness and activity-density. Assemblage composition changed along the altitudinal gradient, and both richness and activity-density increased with elevation. Thus, carabid communities, including montane specialists and several species of conservation interest, stand to benefit from close-to-nature and retention practices, if applied throughout the altitude range of montane forests. Forest carabids may additionally profit from maintaining higher roe deer abundances, but further research is needed to understand this causal link, as well as to weigh the costs and benefits of deer culling for forest biodiversity.</p>
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>
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F).
Fig. 1 in Individual Movement Of Large Carabids As A Link For Activity Density Patterns In Various Forestry Treatments
Fig. 1. Mean activity density of Carabus scheidleri (a) and C. coriaceus (b) per sampling plot in different for- estry treatments (C = control, CC = clear-cutting, P = preparation cut- ting) between 2014 and 2018. Verti- cal lines represent a 95% confidence interval and capital letters above bars indicate significant differences based on Tukey's multiple compari- sons of means
Fig. 2 in Individual Movement Of Large Carabids As A Link For Activity Density Patterns In Various Forestry Treatments
Fig. 2. Movements of Carabus scheidleri (a) within and between forestry treatments (C = control, CC = clear-cutting, P = preparation cutting) based on CMR. The number next to the arrow corresponds with the number of recorded movements. Individual trajectories of radio-tracked C. coriaceus (b) in the experimental area, black dots represent the first release point for each trajectory
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.