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opencc-by-4.0Feb 2022View details →

Tree ring width chronologies of four Pinaceae species in boreal forests in Yakutia in 2018

Tree cores and discs were collected during fieldwork in Yakutia in 2018 by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia (Kruse et al., 2019). The samples were dried, sanded, digitized and further processed by identifying the ring layers end exporting the tree ring width for each year. The site chronologies were established by cross-dating all samples to each other, which helped coping with small ring sizes but especially with missing rings, and frost rings. We processed samples of four species, Larix gmelinii (LAGM), Picea obovata (PIOB), Pinus sylvestris (PISY) and Pinus sibirica (PISI). These were recorded at a variety of locations: <ul> <li>LAGM from Lake Khamra sites EN18079, -80, -81, -83 (N59.974919° E112.958985°, N59.977106° E112.961379°, N59.970583° E112.987096°, N59.974714° E113.002874°)</li> <li>PIOB from Lake Khamra sites EN18079, -81, -83 (59.974919° E112.958985°, 59.970583° E112.987096°, 59.974714° E113.002874°)</li> <li>PISI from Lake Khamra site EN18080 (N59.977106° E112.961379°)</li> <li>PISY from different sites between EN18061 (N62.076376° E129.618586°) and EN18077 (N61.892568° E114.288623°)</li> </ul> Data format The data consists of one file in dendrochronological TUCSON format without header for each of the four tree species. Additional information This data is linked to further information about individual trees and their sites as published in: van Geffen, Femke; Schulte, Luise; Geng, Rongwei; Heim, Birgit; Pestryakova, Luidmila A; Herzschuh, Ulrike; Kruse, Stefan (2021): Tree height and crown diameter during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.932817 and an extension to: Shevtsova, Iuliia; Kruse, Stefan; Herzschuh, Ulrike; Brieger, Frederic; Schulte, Luise; Stuenzi, Simone Maria; Pestryakova, Luidmila A; Zakharov, Evgenii S (2020): Individual tree and tall shrub partial above-ground biomass of central Chukotka in 2018. PANGAEA, https://doi.org/10.1594/PANGAEA.923784 Information about the expedition in 2018 in: Kruse, Stefan; Bolshiyanov, Dimitry Yu; Grigoriev, Mikhail N; Morgenstern, Anne; Pestryakova, Ludmila A; Tsibizov, Leonid; Udke, Annegret (2019): Russian-German Cooperation: Expeditions to Siberia in 2018. Berichte zur Polar- und Meeresforschung = Reports on Polar and Marine Research, 734, 257 pp, https://doi.org/10.2312/BzPM_0734_2019

opencc-by-4.0Feb 2022View details →

Individual tree aboveground biomass of four Pinaceae species in boreal forests in Yakutia in 2018

Samples to estimate aboveground tree biomass for four boreal forest species (Larix gmelinii, Picea obovata, Pinus sylvestris, Pinus sibirica) were collected during fieldwork in Yakutia in 2018 by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia (Kruse et al., 2019). From each of the visited site, three living trees (a small, a medium-sized and the talles tree) per each site were cut down after estimating the quantity of the different types to be sampled, namely branches, needles, cones, making up the tree. Further, to estimate the stem weight, tree discs were taken. The discs were taken at the base of a tree (0 cm, disc A), breast height (130 cm, disc B) and top/close to the top of a tree (260 cm, disc C). If the tree was small with <1.3 m, its stem is included as woody biomass in the branch sample. To estimate each tree's stem biomass, the stem was assumed to have a cone shape. Dead trees were also sampled, if present. All harvested samples were weighed fresh in the field and subsampled. The dry weight of all subsamples was recorded after oven drying (60 °C, 48 h for needle and branch samples, up to one week for tree stem discs). A detailed protocol for total tree and shrub AGB estimation can be found in Shevtsova, et al. (2020). Data format The data consists of one table for each of the four species. The columns (N=13) contain the follwoing information: 1. TreeDataBaseID -> unique Tree Data Base identifier of the individual 2. Site     -> Sampling site name 3. SampleID -> Field name given to the individual 4. Species    -> Species name 5. Height_cm -> Height of the tree individual in cm 6. Vitality -> Estimate of the vitality state in 6 levels, ++ very good, + good, 0 mediocre, - bad, -- very bad, dead 7. NeedleWeight_g -> Dry weight of needles in g 8. StemWeight_g    -> Dry weight of the stem in g 9. BiomassBranchStatus -> 1 if branches are present and included in the biomass estimate or not 10. TotalWeightNonStem_g -> Dry weight of all parts but the stem, which are needles, branches and cones in g 11. DiameterBasal_cm -> Stem diameter at tree stem base (0 cm above ground) in cm 12. DiameterBreast_cm -> Stem diameter at breast height (130 cm above ground) in cm 13. CrownDiameter_cm -> Mean crown diameter in cm Additional information This data is linked to further information about individual trees and their sites as published in: van Geffen, Femke; Schulte, Luise; Geng, Rongwei; Heim, Birgit; Pestryakova, Luidmila A; Herzschuh, Ulrike; Kruse, Stefan (2021): Tree height and crown diameter during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.932817 Information about the expedition in 2018 in: Kruse, Stefan; Bolshiyanov, Dimitry Yu; Grigoriev, Mikhail N; Morgenstern, Anne; Pestryakova, Ludmila A; Tsibizov, Leonid; Udke, Annegret (2019): Russian-German Cooperation: Expeditions to Siberia in 2018. Berichte zur Polar- und Meeresforschung = Reports on Polar and Marine Research, 734, 257 pp, https://doi.org/10.2312/BzPM_0734_2019 Aboveground estimation protocol and further data in: Shevtsova, Iuliia; Kruse, Stefan; Herzschuh, Ulrike; Brieger, Frederic; Schulte, Luise; Stuenzi, Simone Maria; Pestryakova, Ludmila A; Zakharov, Evgenii S (2020): Total above-ground biomass of 39 vegetation sites of central Chukotka from 2018. PANGAEA, https://doi.org/10.1594/PANGAEA.923719

opencc-by-4.0Jul 2021View details →

Indicative distribution map for Ecosystem Functional Group T2.1 Boreal and temperate high montane forests and woodlands

This archive contains indicative distribution maps and profiles for T2.1 Boreal and temperate high montane forests and woodlands, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

opencc-by-4.0Mar 2023View details →

Snowpack temperature profile dataset from a boreal forest watershed in eastern Canada.

This dataset presents snow temperature profiles from nine different boreal forest sites in eastern Canada collected over two consecutive winters, 2016-17 and 2017-18. The dataset includes snowpack temperature profiles, snow depth, air temperature, and soil temperature. In addition, the two flux towers provided us with the additional heat and water vapour fluxes. We also present data extracted from intensive snow coring and snowpit surveys, conducted on a weekly and bi-weekly basis. Additionally, stable water isotope data collected from individual snowpack are presented here.  To learn more about the additional information about the data, please read the "Readme.txt" file.

opencc-by-4.0Oct 2023View details →

Simulations for: The anthropogenic imprint on temperate and boreal forest demography and carbon turnover

LPJ-GUESS model output underlying analysis in: Thomas A. M. Pugh, Rupert Seidl, Daijun Liu, Mats Lindeskog, Louise P. Chini, Cornelius Senf, The anthropogenic imprint on temperate and boreal forest demography and carbon turnover, Global Ecology and Biogeography. 10.1111/geb.13773 For a full description of the simulations, please refer to the above paper. If using the data please cite this dataset and the publication above. Files are provided as netcdf4 files. Basic metadata is included in the headers of the individual files. # Simulation types _standard_nat_2014 -> Best estimate simulation under natural disturbance. Averaging period 2001-2014 _high_nat_2014 -> Upper estimate simulation under natural disturbance. Averaging period 2001-2014 _low_nat_2014 -> Lower estimate simulation under natural disturbance. Averaging period 2001-2014 _standard_nat_1990 -> Best estimate simulation under natural disturbance. Averaging period 1961-1990 _standard_natcc_2014 -> Best estimate simulation based on closed-canopy forest area calculations under natural disturbance. Averaging period 2001-2014 _standard_anthro_2014 -> Best estimate simulation under natural and anthropogenic disturbance. Averaging period 2001-2014 _high_anthro_2014 -> Upper estimate simulation under natural and anthropogenic disturbance. Averaging period 2001-2014 _low_anthro_2014 -> Lower estimate simulation under natural and anthropogenic disturbance. Averaging period 2001-2014 _site_recovery_Eurasia_nodist -> Site simulations for 4 Eurasian sites looking at the successional sequence. 800 years long under constant spinup environmental conditions. _site_recovery_America_nodist -> Site simulations for 5 North American sites looking at the successional sequence. 800 years long under constant spinup environmental conditions. # Variables (for units see netcdf metadata) Cveg -> Live vegetation carbon Clitter -> Litter carbon Csoil -> Soil carbon LAI -> Leaf area index NPP -> Net primary productivity GPP -> Gross primary productivity distprob -> Natural disturbance probability age -> Stand age structure temprange -> Annual temperature range (based on monthly means) wooddensity -> community mean wood density Note: All nat simulations assume that forest covers the whole grid cell. All anthro simulations assume that forest only covers the primary and secondary fractions of the grid cell, as defined in the LUH2 dataset, however values are given relative to the whole grid cell. I.e. value_on_forest_area * (primary_area_fraction+secondary_area_fraction).

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera I: Site Attribute Data 2022

This dataset contains site characteristics collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Data includes detailed site characteristics collected at the site level. Each site included three 10 m * 2 m plots (A, B, and C) laid in a single 30 m transect (or, where constrained, in parallel).

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera II: Tree Inventory Data 2022

This dataset contains tree combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Tree species, diameters (DBH where possible, otherwise BD), condition (living/dead, standing/fallen, etc), and component combustion are recorded for every tree in each 10 m * 2 m plot.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera III: Shrub Inventory Data

This dataset contains shrub combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Shrub species, stem diameters (BD), and component combustion were recorded for every shrub in each 10 m * 2 m plot.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IV: Organic Soil Carbon and Nitrogen Content from Organic Soil Samples 2022

This dataset contains lab-quantified (and some field-measured) characteristics for post-fire residual organic soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in summer and fall of 2022 at UAF and NAU.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera V: Organic Soil Depth 2022

This dataset contains field-measured characteristics for post-fire residual organic soil samples and for additional organic soil depths collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VI: Mineral Soil Sample and pH Data 2022

This dataset contains field- and lab-measured characteristics for post-fire mineral soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in fall of 2022 at NAU.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VII: Coarse and Fine Woody Debris Inventory 2022

This dataset contains characteristics of coarse woody debris and snags collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VIII: Seedling Inventory 2022

This dataset contains characteristics of post-fire seedlings and resprouts collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022

This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.

opencc-by-4.0Jun 2020View details →

Habitat suitability predictions for a boreal forest indicator species, the northern goshawk (Accipiter gentilis), in Central Finland

This repository contains files that show optimal sites in Central Finland for the northern goshawk (Accipiter gentilis, hereafter goshawk), an indicator species of boreal forests with conservation values. The optimal sites were derived from the habitat suitability model outputs included in the following publication:   Björklund Heidia, Parkkinen Anssib, Hakkari Tomic, Heikkinen Risto K.d, Virkkala Raimod, Lensu Anssib (2020): Predicting valuable forest habitats using an indicator species for biodiversity. Biological Conservation, <a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> .    a Finnish Museum of Natural History Luomus, P.O. Box 17, FI-00014 University of Helsinki, Finland b University of Jyvaskyla, Department of Biological and Environmental Science, P.O. Box 35, FI-40014 University of Jyvaskyla, Finland c Centre for Economic Development, Transport and the Environment Central Finland, P.O. Box 250, FI-40101 Jyväskylä, Finland d Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland   The files are ArcGIS compatible shape files which indicate the spatial location of the 160 m × 160 m grid cells which include forest stands projected to be either highly suitable or suitable as a nesting site for the goshawk in Central Finland. The habitat suitability models and values were developed across the study area using Maxent software. The files show those 160-m grid cells from the study area which were included in one of the following two categories: (i) cells deemed as the most optimal (with high probability of suitable conditions) for goshawk nesting with suitability index values in Maxent outputs varying between 0.92–1.00 (‘best’ goshawk squares), and (ii) cells deemed as ‘good’ goshawk squares (with Maxent suitability index values of ≥ 0.69 and < 0.92). The coordinate system for the data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)).  Summarization of the key settings and elements of the study are provided below. A detailed treatment can now be found in the article published in Biological Conservation (Björklund et al.) for which the link is the following: <a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> .   Summary of the study Intensive commercial use of boreal forests is an accelerating threat to forest biodiversity, highlighting the development of cost-effective tools to detect the locations valuable for conservation. We applied species distribution models (SDMs) in our study area, Central Finland, to locate the optimal nesting sites for the goshawk, an indicator bird species for biodiversity hotspots in mature boreal forests. The optimal sites (here, 160 x 160 m grid squares) for the goshawk were determined using the Maxent software. Optimal squares for the goshawk had forests with considerably high volumes of Norway spruce (Picea abies, hereafter spruce) covering only 3.4% of the boreal landscape, and they were located mostly outside protected areas. Many of the squares with optimal nesting forests appeared to be under threat due to recently intensified logging operations. Half of the squares were logged to some extent and 10% were already lost or notably deteriorated due to logging after 2015 for which our models were calibrated. Threats to biodiversity of mature boreal spruce forests are likely to accelerate with increasing logging pressures. Thus, there is an urgent need to secure the continuous supply of mature spruce forests in the landscape by developing a denser network of protected areas and applying measures that aid in sparing large entities of mature forest on privately-owned land. Our modelled optimal squares can be used for selection of potential areas with biodiversity values in conservation prioritization. The study species The goshawk is a raptor species which prefers mature forests for nesting in Europe. Old forests dominated by spruce are considered as important for the breeding success of the species particularly in northern latitudes. Thus, intensive forest management can impair the breeding possibilities of the goshawk, and changes in forest landscapes are likely to contribute to the decline of the species. For example, in Finland, the goshawk is classified as nearly threatened species. In our study, we used the goshawk as an indicator species to model the spatial locations of boreal forest with much potential for including biodiversity values. The indicator species status of the goshawk is based on earlier studies showing the close association of the goshawk with various taxa of mature spruce forest, as well as the reported declines of both the goshawk and associated species due to loggings. Developing Maxent models for the goshawk The location data on occupied nests of the goshawk gathered in spring and summer 2015 and 2016 in Central Finland – as a part of the Finnish Common Birds of Prey Monitoring – were related to a set of environmental predictor variables using a maximum entropy method, Maxent software, which is considered particularly useful for modelling presence-only data (such as our goshawk nest site data). In our case, the data on forest stand and tree characteristics were related using Maxent to the known nesting sites to predict suitable conditions for the species across the Central Finland. The forest data used in the modelling were extracted from the multi-source national forest inventory (MS-NFI) data sources governed by the Natural Resources Institute Finland. The MS-NFI data used in our modelling are based on field data of the 11th and 12th NFIs from 2009 to 2016 and satellite images from 2015 and 2016. Prior modelling, Pearson correlations were calculated between the continuous environmental variables at the nest sites. Of the highly (|r| ≥ 0.7) correlated variables, we chose those variables which are known to be important for the goshawk, which are useful for generalization in other areas, or whose impact was of specific interest. Our final selected set of predictor variables included one class variable, site fertility class, and nine continuous variables: growing stock volume of the spruce, pine, birches and other hardwood, canopy cover, canopy cover of broad-leaved trees, saw timber of other broad-leaved trees than birches, pulpwood volume of the birches, and the biomass of the stem residual of the spruce. The original MS-NFI data recorded at the resolution of 16 × 16 m were resampled to the resolution of 160 × 160 m for the Maxent models, to represent one potential nesting forest stand. The accuracy of Maxent models were assessed with cross-validation and associated averaged AUC-values. The relative importance of the variables was measured by variable contribution and model deterioration measures provided by Maxent. The cloglog-transformed output index values ranging from 0 to 1 described the relative suitability of the 160-m squares to goshawk nesting. Based on the index values, the squares were classified as ‘optimal’ (with index values of 0.69–1.00), ‘typical’ (0.46– <0.69) and ‘poor’ (<0.46). In addition, we divided optimal squares into ‘best’ goshawk squares (index values of 0.92–1.00 corresponding to a high probability of suitable conditions), and ‘good’ goshawk squares (index values ≥ 0.69 and < 0.92). Maxent model outputs Spruce volume was the most important variable in defining habitat suitability for goshawk nesting, but hardwood cover, other hardwood logs and site fertility class contributed also to some extent to habitat suitability. In Maxent outputs, the set of 160-m squares deemed as optimal for goshawk nesting included 6 895 (cover 0.9% of the study area) best goshawk squares and 19 421 (cover 2.5%) good goshawk squares. The projected best and good goshawk squares were mostly located in unprotected areas: 95.0% of the best and 96.0% of the good goshawk squares occurred completely outside protected areas. For further details concerning the data and the model outputs, see the referred article Björklund et al. (2020). State of the optimal goshawk squares In total, 11% of best and over 9% of good goshawk squares were severely altered due to recent harvesting, typically clear-cutting, of the forests during the time period between 2015 and 2019. Altogether, some level of logging occurred in 3 062 (44%) of best goshawk and 9 846 (51%) of good goshawk squares during the recent years. However, many of the squares still included enough unlogged area for the goshawk in 2019. In our article, we conclude that while most of the optimal squares for the goshawk were still preserved in 2019, they are under risk as they are mainly situated outside protected area network. This stresses the importance of conserving biodiversity with complementary measures in privately-owned managed forests. In conclusion, a denser network with more PAs for forest-dwelling species should be secured in areas with intensive forestry, e.g. in southern Finland where PAs currently cover a smaller proportion of land compared to northern Finland.

opencc-by-4.0Jan 2021View details →

Data from: Choosy beetles: how host trees and southern boreal forest naturalness may determine dead wood beetle communities

See methods section of paper for detailed information on dataset and sources; briefly, these .csv files includes numbers of each beetle species captured at all sites used in the project, as well as information about each site and about each species.   Data from: Choosy beetles: how host trees and southern boreal forest naturalness may determine dead wood beetle communities Ryan C. Burner, Tone Birkemoe, Jörg G. Stephan, Lukas Drag, Jörg Muller, Otso Ovakainen, Mária Potterf, Olav Skarpaas, Tord Snall, Anne Sverdrup-Thygeson Forest Ecology and Management, 2021   From abstract of paper: Wood-living beetles make up a large proportion of forest biodiversity, and contribute to important ecosystem services, including decomposition. Beetle communities in managed southern boreal forests are less species rich than in natural and near-natural forest stands. In addition, many beetle species rely primarily on specific tree species. Yet, the associations between individual beetle species, forest management category, and tree species are seldom quantified, even for red-listed beetles. We compiled a beetle capture dataset from flight intercept traps placed in Norway spruce (Picea abies), oak (Quercus sp.), and Eurasian aspen (Populus tremulae) trees in 413 sites in mature managed forest, near-natural forest, and clear-cuts in southeastern Norway. We used joint species distribution models to estimate the strength of associations for 368 saproxylic beetle species (including 20 vulnerable, endangered, or critical red-listed species) for each forest management category and tree species. Tree species on which traps were mounted had the largest effect on beetle communities; oaks had the most highly associated beetle species, including most of the red-listed species, followed by Norway spruce and Eurasian aspen. Most beetle species were more likely to be captured in near-natural than in mature managed forest. Our estimated associations were compatible – for many species – with categorical classifications found in several existing databases of saproxylic beetle preferences. These quantitative beetle-habitat associations will improve future analyses that have typically relied on categorical classifications. Our results highlight the need to prioritize conservation of near-natural forests and oak trees in Scandinavia to protect the habitat of many red-listed species in particular. Furthermore, we underline the importance of carefully considering the species of trees on which traps are mounted in order to representatively sample beetle communities in forest stands.

opencc-by-4.0Nov 2023View details →

Dataset for "Heatwave reveals potential for enhanced aerosol formation in Siberian boreal forest"

This dataset supplements the manuscript "Heatwave reveals potential for enhanced aerosol formation in Siberian boreal forest", Environmental Research Letters, 2023.

opencc-by-4.0Oct 2024View details →

Distribution and Characteristics of Lightning-Ignited Wildfires in Boreal Forests - the BoLtFire database

This repository holds a dataset of lightning-ignited wildfires across the boreal biome. The BoLtFire dataset covers the period 2012 to 2022 and encompasses 6,902 fires - 4,201 in Eurasia and 2,701 in North America. The layers included in this dataset are: FireID, StartDate, EndDate, FireYdear, AreaHa (burned area), ClassSize, BiomeName, EcoBiome, EcoName, EcoID, Realm, LCDN (Land cover number), LCName (land cover name), Country, Continent, HoldoverD (days), HoldoverRD (holdover rounded), IgnLat (Ignition location Latitude), IgnLong (Ignition Location Longitude), DisPol (Distance of the ignition location to the fire perimeter if it is located outside the polygon), and PerCheck (designates if the ignition location is within the fire perimeter or oustide the perimeter).   The datasets are available per continent (North America, Europe, and Asia) as shapefiles. The spatial reference system is Global LANd Cover mapping and Estimation (GLANCE) Grids - Version 01 CRS.   *Please note: Versions 1 and 2 are missing LIW from Canada between 2021-2022.